diff --git a/.github/.codecov.yml b/.github/.codecov.yml deleted file mode 100644 index 0c6bd02622..0000000000 --- a/.github/.codecov.yml +++ /dev/null @@ -1,3 +0,0 @@ -ignore: - - "setup.py" - - "src/qibo/tests/test_custom_functions.py" diff --git a/.github/codecov.yml b/.github/codecov.yml new file mode 100644 index 0000000000..5571132e2b --- /dev/null +++ b/.github/codecov.yml @@ -0,0 +1,8 @@ +coverage: + status: + project: + default: + threshold: 0.5% + +github_checks: + annotations: false diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 1399089483..c0b9fdf544 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -1,4 +1,4 @@ -name: Deploy docs +name: docs on: workflow_dispatch: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 28dfdd0dfc..9e5265c24b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,7 +9,7 @@ repos: - id: check-yaml - id: debug-statements - repo: https://github.com/psf/black - rev: 24.1.1 + rev: 24.2.0 hooks: - id: black - repo: https://github.com/pycqa/isort @@ -18,7 +18,7 @@ repos: - id: isort args: ["--profile", "black"] - repo: https://github.com/asottile/pyupgrade - rev: v3.15.0 + rev: v3.15.1 hooks: - id: pyupgrade - repo: https://github.com/hadialqattan/pycln diff --git a/README.md b/README.md index bdb1e6f4f8..46a64fe115 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,8 @@ ![Logo](https://github.com/qiboteam/qibo/blob/master/doc/source/_static/qibo_logo_dark.svg) -![Tests](https://github.com/qiboteam/qibo/workflows/Tests/badge.svg) [![codecov](https://codecov.io/gh/qiboteam/qibo/branch/master/graph/badge.svg?token=1EKZKVEVX0)](https://codecov.io/gh/qiboteam/qibo) -[![DOI](https://zenodo.org/badge/241307936.svg)](https://zenodo.org/badge/latestdoi/241307936) -[![Matrix](https://img.shields.io/matrix/qibo%3Amatrix.org?logo=matrix)](https://matrix.to/#/#qibo:matrix.org) +![PyPI - Version](https://img.shields.io/pypi/v/qibo) +![PyPI - Python Version](https://img.shields.io/pypi/pyversions/qibo) Qibo is an open-source full stack API for quantum simulation and quantum hardware control. @@ -15,6 +14,8 @@ Some of the key features of Qibo are: ## Documentation +[![docs](https://github.com/qiboteam/qibo/actions/workflows/publish.yml/badge.svg)](https://qibo.science/qibo/stable/) + Qibo documentation is available [here](https://qibo.science). ## Minimum Working Examples @@ -49,6 +50,8 @@ result = c(nshots=100) In both cases, the simulation will run in a single device CPU or GPU in double precision `complex128`. ## Citation policy +[![arXiv](https://img.shields.io/badge/arXiv-2009.01845-b31b1b.svg)](https://arxiv.org/abs/2009.01845) +[![DOI](https://zenodo.org/badge/241307936.svg)](https://zenodo.org/badge/latestdoi/241307936) If you use the package please refer to [the documentation](https://qibo.science/qibo/stable/appendix/citing-qibo.html#publications) for citation instructions. @@ -56,7 +59,7 @@ If you use the package please refer to [the documentation](https://qibo.science/ To get in touch with the community and the developers, consider joining the Qibo workspace on Matrix: -https://matrix.to/#/#qibo:matrix.org +[![Matrix](https://img.shields.io/matrix/qibo%3Amatrix.org?logo=matrix)](https://matrix.to/#/#qibo:matrix.org) ## Supporters and collaborators diff --git a/doc/source/_static/comp_basis_encoder.png b/doc/source/_static/comp_basis_encoder.png new file mode 100644 index 0000000000..eb02139534 Binary files /dev/null and b/doc/source/_static/comp_basis_encoder.png differ diff --git a/doc/source/_static/phase_encoder.png b/doc/source/_static/phase_encoder.png new file mode 100644 index 0000000000..df6796e1d9 Binary files /dev/null and b/doc/source/_static/phase_encoder.png differ diff --git a/doc/source/_static/unary_encoder_ladder.png b/doc/source/_static/unary_encoder_ladder.png new file mode 100644 index 0000000000..fd7672b182 Binary files /dev/null and b/doc/source/_static/unary_encoder_ladder.png differ diff --git a/doc/source/_static/unary_encoder_tree.png b/doc/source/_static/unary_encoder_tree.png new file mode 100644 index 0000000000..dd17b20c8e Binary files /dev/null and b/doc/source/_static/unary_encoder_tree.png differ diff --git a/doc/source/api-reference/qibo.rst b/doc/source/api-reference/qibo.rst index 980faf2c70..3457ad0c0c 100644 --- a/doc/source/api-reference/qibo.rst +++ b/doc/source/api-reference/qibo.rst @@ -270,18 +270,119 @@ For instance, the following two circuit generations are equivalent: circuit_2.add(gates.X(2)) +.. image:: ../_static/comp_basis_encoder.png + :width: 3400px + :height: 2000px + :scale: 25 % + :align: center + + .. autofunction:: qibo.models.encodings.comp_basis_encoder +Phase Encoder +""""""""""""" + +Encodes data of length :math:`n` into the phases of :math:`n` qubits. + + +For instance, the following two circuit generations are equivalent: + +.. testsetup:: + + import numpy as np + + from qibo import Circuit, gates + from qibo.models.encodings import phase_encoder + +.. testcode:: + + nqubits = 3 + phases = np.random.rand(nqubits) + + circuit_1 = phase_encoder(phases, rotation="RX") + + circuit_2 = Circuit(3) + circuit_2.add(gates.RX(qubit, phases[qubit]) for qubit in range(nqubits)) + + +.. image:: ../_static/phase_encoder.png + :width: 1333px + :height: 1552px + :scale: 30 % + :align: center + + +.. autofunction:: qibo.models.encodings.phase_encoder + + Unary Encoder """"""""""""" +Given a classical ``data`` array :math:`\mathbf{x} \in \mathbb{R}^{d}` such that + +.. math:: + \mathbf{x} = (x_{1}, x_{2}, \dots, x_{d}) \, , + +this function generate the circuit that prepares the following quantum state +:math:`\ket{\psi} \in \mathcal{H}`: + +.. math:: + \ket{\psi} = \frac{1}{\|\mathbf{x}\|_{\textup{HS}}} \, + \sum_{k=1}^{d} \, x_{k} \, \ket{k} \, , + +with :math:`\mathcal{H} \cong \mathbb{C}^{d}` being a :math:`d`-qubit Hilbert space, +and :math:`\|\cdot\|_{\textup{HS}}` being the Hilbert-Schmidt norm. + +Here, :math:`\ket{k}` is a unary representation of the number :math:`k`. +For instance, for :math:`d = 3`, the final state would be + +.. math:: + \ket{\psi} = \frac{1}{\|\mathbf{x}\|_{\textup{HS}}} \, + \left( x_{1} \ket{001} + x_{2} \ket{010} + x_{3} \ket{100} \right) \, . + +There are multiple circuit architechtures that lead to unary encoding of classical data. +For example, to encode a :math:`8`-dimensional data, one could use the so-called +*tree* architechture below: + +.. image:: ../_static/unary_encoder_tree.png + :width: 1333px + :height: 1552px + :scale: 30 % + :align: center + +where the first gate is the :class:`qibo.gates.X` +and the parametrized gates are the :class:`qibo.gates.RBS`. +To know how the angles :math:`\{\theta_{k}\}_{[k]}` are calculated for this architecture, +please refer to S. Johri *et al.*, *Nearest Centroid Classification on a Trapped Ion Quantum Computer*, +`arXiv:2012.04145v2 [quant-ph] `_. + +On the other hand, the same encoding could be performed using the so-called +*diagonal* (also known as *ladder*) architecture below: + +.. image:: ../_static/unary_encoder_ladder.png + :width: 1867px + :height: 1552px + :scale: 30 % + :align: center + +This architecture leads to a choice of angles based on +`spherical coordinates in a d-dimensional hypersphere +`_. + + .. autofunction:: qibo.models.encodings.unary_encoder Unary Encoder for Random Gaussian States """""""""""""""""""""""""""""""""""""""" +Performs the same unary encoder as :class:`qibo.models.encodings.unary_encoder` +using the *tree* architecture , with the difference being that now each entry +of the :math:`d`-dimensional array is sampled from a Gaussian distribution +:math:`\mathcal{N}(0, 1)`. + + .. autofunction:: qibo.models.encodings.unary_encoder_random_gaussian @@ -1515,22 +1616,10 @@ The destabilizers can be extracted analogously with :meth:`qibo.quantum_info.cli :member-order: bysource -Metrics -^^^^^^^ - -Set of functions that are used to calculate metrics of states, (pseudo-)distance measures -between states, and distance measures between quantum channels. - -Purity -"""""" - -.. autofunction:: qibo.quantum_info.purity - - -Impurity -"""""""" +Entanglement measures +^^^^^^^^^^^^^^^^^^^^^ -.. autofunction:: qibo.quantum_info.impurity +Set of functions to calculate entanglement measures. Concurrence @@ -1545,45 +1634,149 @@ Entanglement of formation .. autofunction:: qibo.quantum_info.entanglement_of_formation -Entropy -""""""" +Entanglement fidelity +""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.entanglement_fidelity + + +Meyer-Wallach entanglement +"""""""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.meyer_wallach_entanglement + + +Entanglement capability +""""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.entangling_capability + + +Entropy measures +^^^^^^^^^^^^^^^^ + +Set of functions to calculate entropy measures. + + +Shannon entropy +""""""""""""""" + +.. autofunction:: qibo.quantum_info.shannon_entropy + + +Classical relative entropy +"""""""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.classical_relative_entropy + + +Classical Rényi entropy +""""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.classical_renyi_entropy + + +Classical Rényi relative entropy +"""""""""""""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.classical_relative_renyi_entropy + + +Classical Tsallis entropy +""""""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.classical_tsallis_entropy + + +von Neumann entropy +""""""""""""""""""" -.. autofunction:: qibo.quantum_info.entropy +.. autofunction:: qibo.quantum_info.von_neumann_entropy .. note:: - ``validate`` flag allows the user to choose if the function will check if input - ``state`` is Hermitian or not. Default option is ``validate=False``, i.e. the - assumption of Hermiticity, because it is faster and, more importantly, - the functions are intended to be used on Hermitian inputs. When ``validate=True`` + ``check_hermitian`` flag allows the user to choose if the function will check if input + ``state`` is Hermitian or not. Default option is ``check_hermitian=False``, i.e. the + assumption of Hermiticity. This is faster and, more importantly, + this function are intended to be used on Hermitian inputs. When ``check_hermitian=True`` and ``state`` is non-Hermitian, an error will be raised when using `cupy` backend. +Relative von Neumann entropy +"""""""""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.relative_von_neumann_entropy + +.. note:: + ``check_hermitian`` flag allows the user to choose if the function will check if input + ``state`` is Hermitian or not. Default option is ``check_hermitian=False``, i.e. the + assumption of Hermiticity. This is faster and, more importantly, + this function are intended to be used on Hermitian inputs. When ``check_hermitian=True`` + and either ``state`` or ``target`` is non-Hermitian, + an error will be raised when using `cupy` backend. + + +Rényi entropy +""""""""""""" + +.. autofunction:: qibo.quantum_info.renyi_entropy + + +Relative Rényi entropy +"""""""""""""""""""""" + +.. autofunction:: qibo.quantum_info.relative_renyi_entropy + + +Tsallis entropy +""""""""""""""" + +.. autofunction:: qibo.quantum_info.tsallis_entropy + + Entanglement entropy """""""""""""""""""" .. autofunction:: qibo.quantum_info.entanglement_entropy .. note:: - ``validate`` flag allows the user to choose if the function will check if + ``check_hermitian`` flag allows the user to choose if the function will check if the reduced density matrix resulting from tracing out ``bipartition`` from input - ``state`` is Hermitian or not. Default option is ``validate=False``, i.e. the - assumption of Hermiticity, because it is faster and, more importantly, - the functions are intended to be used on Hermitian inputs. When ``validate=True`` + ``state`` is Hermitian or not. Default option is ``check_hermitian=False``, i.e. the + assumption of Hermiticity. This is faster and, more importantly, + this function are intended to be used on Hermitian inputs. When ``check_hermitian=True`` and the reduced density matrix is non-Hermitian, an error will be raised when using `cupy` backend. +Metrics +^^^^^^^ + +Set of functions that are used to calculate metrics of states, (pseudo-)distance measures +between states, and distance measures between quantum channels. + +Purity +"""""" + +.. autofunction:: qibo.quantum_info.purity + + +Impurity +"""""""" + +.. autofunction:: qibo.quantum_info.impurity + + Trace distance """""""""""""" .. autofunction:: qibo.quantum_info.trace_distance .. note:: - ``validate`` flag allows the user to choose if the function will check if difference + ``check_hermitian`` flag allows the user to choose if the function will check if difference between inputs, ``state - target``, is Hermitian or not. Default option is - ``validate=False``, i.e. the assumption of Hermiticity, because it is faster and, + ``check_hermitian=False``, i.e. the assumption of Hermiticity, because it is faster and, more importantly, the functions are intended to be used on Hermitian inputs. - When ``validate=True`` and ``state - target`` is non-Hermitian, an error will be + When ``check_hermitian=True`` and ``state - target`` is non-Hermitian, an error will be raised when using `cupy` backend. @@ -1617,12 +1810,6 @@ Bures distance .. autofunction:: qibo.quantum_info.bures_distance -Entanglement fidelity -""""""""""""""""""""" - -.. autofunction:: qibo.quantum_info.entanglement_fidelity - - Process fidelity """""""""""""""" @@ -1653,18 +1840,6 @@ Diamond Norm .. autofunction:: qibo.quantum_info.diamond_norm -Meyer-Wallach entanglement -"""""""""""""""""""""""""" - -.. autofunction:: qibo.quantum_info.meyer_wallach_entanglement - - -Entanglement capability -""""""""""""""""""""""" - -.. autofunction:: qibo.quantum_info.entangling_capability - - Expressibility of parameterized quantum circuits """""""""""""""""""""""""""""""""""""""""""""""" @@ -1680,6 +1855,13 @@ Frame Potential Quantum Networks ^^^^^^^^^^^^^^^^ +Quantum network is an object that unifies the representation of quantum states, channels, +observables, and higher-order quantum operators. + +For more details, see G. Chiribella *et al.*, *Theoretical framework for quantum networks*, +`Physical Review A 80.2 (2009): 022339 +`_. + .. autoclass:: qibo.quantum_info.quantum_networks.QuantumNetwork :members: :member-order: bysource @@ -2075,18 +2257,6 @@ Hadamard Transform .. autofunction:: qibo.quantum_info.hadamard_transform -Shannon entropy -""""""""""""""" - -.. autofunction:: qibo.quantum_info.shannon_entropy - - -Total Variation distance -"""""""""""""""""""""""" - -.. autofunction:: qibo.quantum_info.total_variation_distance - - Hellinger distance """""""""""""""""" diff --git a/doc/source/appendix/citing-qibo.rst b/doc/source/appendix/citing-qibo.rst index 40f8a3b456..7d08dc2e2d 100644 --- a/doc/source/appendix/citing-qibo.rst +++ b/doc/source/appendix/citing-qibo.rst @@ -69,8 +69,28 @@ Peer-Reviewed Articles * S. Efthymiou, A. Orgaz-Fuertes, R. Carobene, J. Cereijo, A. Pasquale, S. Ramos-Calderer, S. Bordoni, D. Fuentes-Ruiz, A. Candido, E. Pedicillo, M. Robbiati, Y.P. Tan, J. Wilkens, I. Roth, J.I. Latorre, S. Carrazza, *Qibolab: - an open-source hybrid quantum operating system* (2023), (`arXiv:2308.06313`_). + an open-source hybrid quantum operating system* (2023), + `doi:10.22331/q-2024-02-12-1247`_, (`arXiv:2308.06313`_). + In *BibTeX* format: + + .. code-block:: text + + @article{qibolab_paper, + doi = {10.22331/q-2024-02-12-1247}, + url = {https://doi.org/10.22331/q-2024-02-12-1247}, + title = {Qibolab: an open-source hybrid quantum operating system}, + author = {Efthymiou, Stavros and Orgaz-Fuertes, Alvaro and Carobene, Rodolfo and Cereijo, Juan and Pasquale, Andrea and Ramos-Calderer, Sergi and Bordoni, Simone and Fuentes-Ruiz, David and Candido, Alessandro and Pedicillo, Edoardo and Robbiati, Matteo and Tan, Yuanzheng Paul and Wilkens, Jadwiga and Roth, Ingo and Latorre, Jos{\'{e}} Ignacio and Carrazza, Stefano}, + journal = {{Quantum}}, + issn = {2521-327X}, + publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, + volume = {8}, + pages = {1247}, + month = feb, + year = {2024} + } + +.. _`doi:10.22331/q-2024-02-12-1247`: https://doi.org/10.22331/q-2024-02-12-1247 .. _`arXiv:2308.06313`: https://arxiv.org/abs/2308.06313 * R. Carobene, A. Candido, J. Serrano, A.O-Fuertes, A. Giachero, S. Carrazza, diff --git a/doc/source/code-examples/tutorials/quantum_networks/README.md b/doc/source/code-examples/tutorials/quantum_networks/README.md new file mode 100644 index 0000000000..e0b074640c --- /dev/null +++ b/doc/source/code-examples/tutorials/quantum_networks/README.md @@ -0,0 +1,122 @@ +# Quantum Networks + +## The Quantum Network Model + +The quantum network model is a mathematical framework that allows us to uniquely describe quantum information processing that involves multiple points in time and space. +Each distinguished point in time and space is treated as a linear system $\mathcal{H}_ i$. +A quantum network involving $n$ points in time and space is a Hermitian operator $\mathcal{N}$ that acts on the tensor product of the linear systems $\mathcal{H}_ 0 \otimes \mathcal{H}_ 1 \otimes \cdots \otimes \mathcal{H}_ {n-1}$. +Each system $\mathcal{H}_ {i}$ is either an input or an output of the network. + +A physically implementable quantum network is described by a semi-positive definite operator $\mathcal{N}$ that satisfies the causal constraints. + +A simple example is a quantum channel $\Gamma: \mathcal{H}_ 0 \to \mathcal{H}_ 1$, where $\mathcal{H}_ 0$ is the input system and $\mathcal{H}_ 1$ is the output system. +The quantum channel is a linear map, such that it maps any input quantum state to an output quantum state, which is a sufficient and necessary condition for the map to be physical. +A Hermitian operator $J^\Gamma$ acting on $\mathcal{H}_ 0\otimes \mathcal{H}_ 1$ is associated with a quantum channel $\Gamma$, if $J^\Gamma$ satisfies the following conditions: +$$J^\Gamma \geq 0, \quad \text{and} \quad \text{Tr}_ {\mathcal{H}_ 1} J^\Gamma = \mathbb{I}_ {\mathcal{H} _0} .$$ + +The first condition is called *complete positivity*, and the second condition is called *trace-preserving*. +In particular, the second condition ensures that the information of the input system is only accessible through the output system. + +In particular, a quantum state $\rho$ may be also considered as a quantum network, where the input system is the trivial system $\mathbb{C}$, and the output system is the quantum system $\mathcal{H}$. +The constraints on the quantum channels are then equivalent to the constraints on the quantum states: +$$\rho \geq 0, \quad \text{and} \quad \text{Tr} \rho = \mathbb{I}_ \mathbb{C} = 1\ .$$ + +> For more details, see G. Chiribella *et al.*, *Theoretical framework for quantum networks*, +> [Physical Review A 80.2 (2009): 022339](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.80.022339). + +## Quantum Network in `qibo` + +The manipulation of quantum networks in `qibo` is done through the `QuantumNetwork` class. + +```python +from qibo.quantum_info.quantum_networks import QuantumNetwork +``` + +A quantum state is a quantum network with a single input system and a single output system, where the input system is the trivial 1-dimensional system. +We need to specify the dimensions of each system in the `partition` argument. + +```python +from qibo.quantum_info import random_density_matrix, random_unitary + +state = random_density_matrix(2) +state_choi = QuantumNetwork(state, (1,2)) +print(f'A quantum state is a quantum netowrk of the form {state_choi}') +``` + +``` +>>> A quantum state is a quantum netowrk of the form J[1 -> 2] +``` + +A general quantum channel can be created in a similar way. + +```python +from qibo.gates import DepolarizingChannel + +test_ch = DepolarizingChannel(0,0.5) +N = len(test_ch.target_qubits) +partition = (2**N, 2**N) + +depolar_choi = QuantumNetwork(test_ch.to_choi(), partition) +print(f'A quantum channel is a quantum netowrk of the form {depolar_choi}') +``` + +``` +>>> A quantum channel is a quantum netowrk of the form J[2 -> 2] +``` + +One may apply a quantum channel to a quantum state, or compose two quantum channels, using the `@` operator. + +```python +new_state = depolar_choi @ depolar_choi @ state_choi +``` + +## Example + +For 3-dimensional systems, an unital channel may not be a mixed unitary channel. + +> Example 4.3 in (Watrous, John. The theory of quantum information. Cambridge university press, 2018.) + +```python +A1 = np.array([ + [0,0,0], + [0,0,1/np.sqrt(2)], + [0,-1/np.sqrt(2),0], +]) +A2 = np.array([ + [0,0,1/np.sqrt(2)], + [0,0,0], + [-1/np.sqrt(2),0,0], +]) +A3 = np.array([ + [0,1/np.sqrt(2),0], + [-1/np.sqrt(2),0,0], + [0,0,0], +]) + +Choi1 = QuantumNetwork(A1, (3,3), pure=True) * 3 +Choi2 = QuantumNetwork(A2, (3,3), pure=True)*3 +Choi3 = QuantumNetwork(A3, (3,3), pure=True)*3 +``` + +The three channels are pure but not unital. Which means they are not unitary. + +```python +print(f"Choi1 is unital: {Choi1.unital()}") +print(f"Choi2 is unital: {Choi2.unital()}") +print(f"Choi3 is unital: {Choi3.unital()}") +``` + +``` +>>> Choi1 is unital: False +Choi2 is unital: False +Choi3 is unital: False +``` + +However, the mixture of the three operators is unital. +As the matrices are orthogonal, they are the extreme points of the convex set of the unital channels. +Therefore, this mixed channel is not a mixed unitary channel. + +```python +Choi = Choi1/3 + Choi2/3 + Choi3/3 +print(f"The mixed channel is unital: {Choi.unital()}") +``` diff --git a/doc/source/conf.py b/doc/source/conf.py index be2efea28d..4937bd5889 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -43,6 +43,7 @@ "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx_copybutton", + "sphinx.ext.viewcode", "recommonmark", "nbsphinx", ] diff --git a/doc/source/getting-started/backends.svg b/doc/source/getting-started/backends.svg index f41f94df97..10890f53b9 100644 --- a/doc/source/getting-started/backends.svg +++ b/doc/source/getting-started/backends.svg @@ -1,3500 +1,6 @@ - - - - - - image/svg+xml - - - - - - - - +
Qibo backends
Simulating on classical hardware
Clifford
Qibotn
Qibojit
tensorflow
numpy
CPU/lightweight
CPU/GPU high performance
Specialized
Executing on quantum hardware
Qibolab
 diff --git a/doc/source/qibo_ecosystem.svg b/doc/source/qibo_ecosystem.svg index c3ace1030f..032c2a7431 100644 --- a/doc/source/qibo_ecosystem.svg +++ b/doc/source/qibo_ecosystem.svg @@ -1,13 +1,13 @@ -
Qibo
Language API
Quantum annealing
Quantum circuits
Quantum information
Implementation
Simulation
Clifford
Specialized in Clifford circuits simulation
Qibotn
TensorNetwork simulator
Qibojit
Efficient device-agnostic simulation with custom operators
tensorflow
Simulation of hybrid QML with automatic differentiation
numpy
Lightweight, fits +well with any CPU
Quantum hardware
Qibocal
Characterization
Calibration
Validation
Qibolab
Control drivers
Convert gates to pulses
Transpiler
= backends
= tools
RFSoCs
Application packages
Qibosoq
Qibochem
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[tool.poetry.group.dev] optional = true diff --git a/src/qibo/backends/__init__.py b/src/qibo/backends/__init__.py index 09e1fe1a54..e329c3c8c2 100644 --- a/src/qibo/backends/__init__.py +++ b/src/qibo/backends/__init__.py @@ -115,6 +115,7 @@ def create(self, dtype): self.SWAP = self.matrices.SWAP self.iSWAP = self.matrices.iSWAP self.SiSWAP = self.matrices.SiSWAP + self.SiSWAPDG = self.matrices.SiSWAPDG self.FSWAP = self.matrices.FSWAP self.ECR = self.matrices.ECR self.SYC = self.matrices.SYC @@ -167,3 +168,10 @@ def set_threads(nthreads): if nthreads < 1: raise_error(ValueError, "Number of threads must be positive.") GlobalBackend().set_threads(nthreads) + + +def _check_backend(backend): + if backend is None: + return GlobalBackend() + + return backend diff --git a/src/qibo/backends/clifford.py b/src/qibo/backends/clifford.py index 9141ca2fd9..8189287f82 100644 --- a/src/qibo/backends/clifford.py +++ b/src/qibo/backends/clifford.py @@ -538,10 +538,9 @@ class CliffordBackend(NumpyBackend): def __init__(self, engine=None): super().__init__() - if engine is None: - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend - engine = GlobalBackend() + engine = _check_backend(engine) if isinstance(engine, TensorflowBackend): raise_error( diff --git a/src/qibo/backends/npmatrices.py b/src/qibo/backends/npmatrices.py index 364845c514..4f180ef4c3 100644 --- a/src/qibo/backends/npmatrices.py +++ b/src/qibo/backends/npmatrices.py @@ -12,53 +12,58 @@ def __init__(self, dtype): self.dtype = dtype self.np = np + def _cast(self, x, dtype): + return self.np.array(x, dtype=dtype) + @cached_property def H(self): - return self.np.array([[1, 1], [1, -1]], dtype=self.dtype) / self.np.sqrt(2) + return self._cast([[1, 1], [1, -1]], dtype=self.dtype) / self.np.sqrt(2) @cached_property def X(self): - return self.np.array([[0, 1], [1, 0]], dtype=self.dtype) + return self._cast([[0, 1], [1, 0]], dtype=self.dtype) @cached_property def Y(self): - return self.np.array([[0, -1j], [1j, 0]], dtype=self.dtype) + return self._cast([[0j, -1j], [1j, 0j]], dtype=self.dtype) @cached_property def Z(self): - return self.np.array([[1, 0], [0, -1]], dtype=self.dtype) + return self._cast([[1, 0], [0, -1]], dtype=self.dtype) @cached_property def SX(self): - return self.np.array([[1 + 1j, 1 - 1j], [1 - 1j, 1 + 1j]], dtype=self.dtype) / 2 + return self._cast([[1 + 1j, 1 - 1j], [1 - 1j, 1 + 1j]], dtype=self.dtype) / 2 @cached_property def SXDG(self): - return self.np.transpose(self.np.conj(self.SX)) + return self._cast([[1 - 1j, 1 + 1j], [1 + 1j, 1 - 1j]], dtype=self.dtype) / 2 @cached_property def S(self): - return self.np.array([[1, 0], [0, 1j]], dtype=self.dtype) + return self._cast([[1 + 0j, 0j], [0j, 1j]], dtype=self.dtype) @cached_property def SDG(self): - return self.np.conj(self.S) + return self._cast([[1 + 0j, 0j], [0j, -1j]], dtype=self.dtype) @cached_property def T(self): - return self.np.array( + return self._cast( [[1, 0], [0, self.np.exp(1j * self.np.pi / 4.0)]], dtype=self.dtype ) @cached_property def TDG(self): - return self.np.conj(self.T) + return self._cast( + [[1, 0], [0, self.np.exp(-1j * self.np.pi / 4.0)]], dtype=self.dtype + ) def I(self, n=2): - return self.np.eye(n, dtype=self.dtype) + return self._cast(self.np.eye(n, dtype=self.dtype), dtype=self.dtype) def Align(self, n=2): - return self.I(n) + return self._cast(self.I(n), dtype=self.dtype) def M(self): # pragma: no cover raise_error(NotImplementedError) @@ -66,45 +71,46 @@ def M(self): # pragma: no cover def RX(self, theta): cos = self.np.cos(theta / 2.0) + 0j isin = -1j * self.np.sin(theta / 2.0) - return self.np.array([[cos, isin], [isin, cos]], dtype=self.dtype) + return self._cast([[cos, isin], [isin, cos]], dtype=self.dtype) def RY(self, theta): cos = self.np.cos(theta / 2.0) + 0j - sin = self.np.sin(theta / 2.0) - return self.np.array([[cos, -sin], [sin, cos]], dtype=self.dtype) + sin = self.np.sin(theta / 2.0) + 0j + return self._cast([[cos, -sin], [sin, cos]], dtype=self.dtype) def RZ(self, theta): phase = self.np.exp(0.5j * theta) - return self.np.array([[self.np.conj(phase), 0], [0, phase]], dtype=self.dtype) + return self._cast([[self.np.conj(phase), 0], [0, phase]], dtype=self.dtype) def GPI(self, phi): phase = self.np.exp(1.0j * phi) - return self.np.array([[0, self.np.conj(phase)], [phase, 0]], dtype=self.dtype) + return self._cast([[0, self.np.conj(phase)], [phase, 0]], dtype=self.dtype) def GPI2(self, phi): phase = self.np.exp(1.0j * phi) - return self.np.array( + return self._cast( [[1, -1.0j * self.np.conj(phase)], [-1.0j * phase, 1]], dtype=self.dtype ) / self.np.sqrt(2) def U1(self, theta): phase = self.np.exp(1j * theta) - return self.np.array([[1, 0], [0, phase]], dtype=self.dtype) + return self._cast([[1, 0], [0, phase]], dtype=self.dtype) def U2(self, phi, lam): eplus = self.np.exp(1j * (phi + lam) / 2.0) eminus = self.np.exp(1j * (phi - lam) / 2.0) - return self.np.array( - [[self.np.conj(eplus), -self.np.conj(eminus)], [eminus, eplus]], + return self._cast( + [[self.np.conj(eplus), -self.np.conj(eminus)], [eminus, eplus]] + / self.np.sqrt(2), dtype=self.dtype, - ) / self.np.sqrt(2) + ) def U3(self, theta, phi, lam): cost = self.np.cos(theta / 2) sint = self.np.sin(theta / 2) eplus = self.np.exp(1j * (phi + lam) / 2.0) eminus = self.np.exp(1j * (phi - lam) / 2.0) - return self.np.array( + return self._cast( [ [self.np.conj(eplus) * cost, -self.np.conj(eminus) * sint], [eminus * sint, eplus * cost], @@ -113,24 +119,31 @@ def U3(self, theta, phi, lam): ) def U1q(self, theta, phi): - return self.U3(theta, phi - self.np.pi / 2, self.np.pi / 2 - phi) + return self._cast( + self.U3(theta, phi - self.np.pi / 2, self.np.pi / 2 - phi), dtype=self.dtype + ) @cached_property def CNOT(self): - return self.np.array( + return self._cast( [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]], dtype=self.dtype ) @cached_property def CY(self): - return self.np.array( - [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, -1j], [0, 0, 1j, 0]], + return self._cast( + [ + [1 + 0j, 0j, 0j, 0j], + [0j, 1 + 0j, 0j, 0j], + [0j, 0j, 0j, -1j], + [0j, 0j, 1j, 0j], + ], dtype=self.dtype, ) @cached_property def CZ(self): - return self.np.array( + return self._cast( [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, -1]], dtype=self.dtype ) @@ -138,7 +151,7 @@ def CZ(self): def CSX(self): a = (1 + 1j) / 2 b = self.np.conj(a) - return self.np.array( + return self._cast( [ [1, 0, 0, 0], [0, 1, 0, 0], @@ -150,65 +163,124 @@ def CSX(self): @cached_property def CSXDG(self): - return self.np.transpose(self.np.conj(self.CSX)) + a = (1 - 1j) / 2 + b = self.np.conj(a) + return self._cast( + [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, a, b], + [0, 0, b, a], + ], + dtype=self.dtype, + ) def CRX(self, theta): - m = self.np.eye(4, dtype=self.dtype) - m[2:, 2:] = self.RX(theta) - return m + cos = self.np.cos(theta / 2.0) + 0j + isin = -1j * self.np.sin(theta / 2.0) + matrix = [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, cos, isin], + [0, 0, isin, cos], + ] + return self._cast(matrix, dtype=self.dtype) def CRY(self, theta): - m = self.np.eye(4, dtype=self.dtype) - m[2:, 2:] = self.RY(theta) - return m + cos = self.np.cos(theta / 2.0) + 0j + sin = self.np.sin(theta / 2.0) + 0j + matrix = [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, cos, -sin], [0, 0, sin, cos]] + return self._cast(matrix, dtype=self.dtype) def CRZ(self, theta): - m = self.np.eye(4, dtype=self.dtype) - m[2:, 2:] = self.RZ(theta) - return m + phase = self.np.exp(0.5j * theta) + matrix = [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, self.np.conj(phase), 0], + [0, 0, 0, phase], + ] + return self._cast(matrix, dtype=self.dtype) def CU1(self, theta): - m = self.np.eye(4, dtype=self.dtype) - m[2:, 2:] = self.U1(theta) - return m + phase = self.np.exp(1j * theta) + matrix = [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, phase], + ] + return self._cast(matrix, dtype=self.dtype) def CU2(self, phi, lam): - m = self.np.eye(4, dtype=self.dtype) - m[2:, 2:] = self.U2(phi, lam) - return m + eplus = self.np.exp(1j * (phi + lam) / 2.0) / self.np.sqrt(2) + eminus = self.np.exp(1j * (phi - lam) / 2.0) / self.np.sqrt(2) + matrix = [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, self.np.conj(eplus), -self.np.conj(eminus)], + [0, 0, eminus, eplus], + ] + return self._cast(matrix, dtype=self.dtype) def CU3(self, theta, phi, lam): - m = self.np.eye(4, dtype=self.dtype) - m[2:, 2:] = self.U3(theta, phi, lam) - return m + cost = self.np.cos(theta / 2) + sint = self.np.sin(theta / 2) + eplus = self.np.exp(1j * (phi + lam) / 2.0) + eminus = self.np.exp(1j * (phi - lam) / 2.0) + matrix = [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, self.np.conj(eplus) * cost, -self.np.conj(eminus) * sint], + [0, 0, eminus * sint, eplus * cost], + ] + return self._cast(matrix, dtype=self.dtype) @cached_property def SWAP(self): - return self.np.array( + return self._cast( [[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]], dtype=self.dtype ) @cached_property def iSWAP(self): - return self.np.array( - [[1, 0, 0, 0], [0, 0, 1j, 0], [0, 1j, 0, 0], [0, 0, 0, 1]], dtype=self.dtype + return self._cast( + [ + [1 + 0j, 0j, 0j, 0j], + [0j, 0j, 1j, 0j], + [0j, 1j, 0j, 0j], + [0j, 0j, 0j, 1 + 0j], + ], + dtype=self.dtype, ) @cached_property def SiSWAP(self): - return self.np.array( + return self._cast( [ - [1, 0, 0, 0], - [0, 1 / self.np.sqrt(2), 1j / self.np.sqrt(2), 0], - [0, 1j / self.np.sqrt(2), 1 / self.np.sqrt(2), 0], - [0, 0, 0, 1], + [1 + 0j, 0j, 0j, 0j], + [0j, 1 / self.np.sqrt(2) + 0j, 1j / self.np.sqrt(2), 0j], + [0j, 1j / self.np.sqrt(2), 1 / self.np.sqrt(2) + 0j, 0j], + [0j, 0j, 0j, 1 + 0j], + ], + dtype=self.dtype, + ) + + @cached_property + def SiSWAPDG(self): + return self._cast( + [ + [1 + 0j, 0j, 0j, 0j], + [0j, 1 / self.np.sqrt(2) + 0j, -1j / self.np.sqrt(2), 0j], + [0j, -1j / self.np.sqrt(2), 1 / self.np.sqrt(2) + 0j, 0j], + [0j, 0j, 0j, 1 + 0j], ], dtype=self.dtype, ) @cached_property def FSWAP(self): - return self.np.array( + return self._cast( [[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, -1]], dtype=self.dtype ) @@ -216,7 +288,7 @@ def fSim(self, theta, phi): cost = self.np.cos(theta) + 0j isint = -1j * self.np.sin(theta) phase = self.np.exp(-1j * phi) - return self.np.array( + return self._cast( [ [1, 0, 0, 0], [0, cost, isint, 0], @@ -231,7 +303,7 @@ def SYC(self): cost = self.np.cos(self.np.pi / 2) + 0j isint = -1j * self.np.sin(self.np.pi / 2) phase = self.np.exp(-1j * self.np.pi / 6) - return self.np.array( + return self._cast( [ [1, 0, 0, 0], [0, cost, isint, 0], @@ -243,7 +315,7 @@ def SYC(self): def GeneralizedfSim(self, u, phi): phase = self.np.exp(-1j * phi) - return self.np.array( + return self._cast( [ [1, 0, 0, 0], [0, complex(u[0, 0]), complex(u[0, 1]), 0], @@ -256,7 +328,7 @@ def GeneralizedfSim(self, u, phi): def RXX(self, theta): cos = self.np.cos(theta / 2.0) + 0j isin = -1j * self.np.sin(theta / 2.0) - return self.np.array( + return self._cast( [ [cos, 0, 0, isin], [0, cos, isin, 0], @@ -269,7 +341,7 @@ def RXX(self, theta): def RYY(self, theta): cos = self.np.cos(theta / 2.0) + 0j isin = -1j * self.np.sin(theta / 2.0) - return self.np.array( + return self._cast( [ [cos, 0, 0, -isin], [0, cos, isin, 0], @@ -281,7 +353,7 @@ def RYY(self, theta): def RZZ(self, theta): phase = self.np.exp(0.5j * theta) - return self.np.array( + return self._cast( [ [self.np.conj(phase), 0, 0, 0], [0, phase, 0, 0], @@ -292,8 +364,8 @@ def RZZ(self, theta): ) def RZX(self, theta): - cos, sin = self.np.cos(theta / 2), self.np.sin(theta / 2) - return self.np.array( + cos, sin = self.np.cos(theta / 2) + 0j, self.np.sin(theta / 2) + 0j + return self._cast( [ [cos, -1j * sin, 0, 0], [-1j * sin, cos, 0, 0], @@ -304,8 +376,8 @@ def RZX(self, theta): ) def RXXYY(self, theta): - cos, sin = self.np.cos(theta / 2), self.np.sin(theta / 2) - return self.np.array( + cos, sin = self.np.cos(theta / 2) + 0j, self.np.sin(theta / 2) + 0j + return self._cast( [ [1, 0, 0, 0], [0, cos, -1j * sin, 0], @@ -318,29 +390,20 @@ def RXXYY(self, theta): def MS(self, phi0, phi1, theta): plus = self.np.exp(1.0j * (phi0 + phi1)) minus = self.np.exp(1.0j * (phi0 - phi1)) - - return self.np.array( + cos = self.np.cos(theta / 2) + 0j + sin = self.np.sin(theta / 2) + 0j + return self._cast( [ - [ - self.np.cos(theta / 2), - 0, - 0, - -1.0j * self.np.conj(plus) * self.np.sin(theta / 2), - ], - [ - 0, - self.np.cos(theta / 2), - -1.0j * self.np.conj(minus) * self.np.sin(theta / 2), - 0, - ], - [0, -1.0j * minus * self.np.sin(theta / 2), self.np.cos(theta / 2), 0], - [-1.0j * plus * self.np.sin(theta / 2), 0, 0, self.np.cos(theta / 2)], + [cos, 0, 0, -1.0j * self.np.conj(plus) * sin], + [0, cos, -1.0j * self.np.conj(minus) * sin, 0], + [0, -1.0j * minus * sin, cos, 0], + [-1.0j * plus * sin, 0, 0, cos], ], dtype=self.dtype, ) def GIVENS(self, theta): - return self.np.array( + return self._cast( [ [1, 0, 0, 0], [0, self.np.cos(theta), -self.np.sin(theta), 0], @@ -355,14 +418,19 @@ def RBS(self, theta): @cached_property def ECR(self): - return self.np.array( - [[0, 0, 1, 1j], [0, 0, 1j, 1], [1, -1j, 0, 0], [-1j, 1, 0, 0]], + return self._cast( + [ + [0j, 0j, 1 + 0j, 1j], + [0j, 0j, 1j, 1 + 0j], + [1 + 0j, -1j, 0j, 0j], + [-1j, 1 + 0j, 0j, 0j], + ], dtype=self.dtype, ) / self.np.sqrt(2) @cached_property def TOFFOLI(self): - return self.np.array( + return self._cast( [ [1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], @@ -372,13 +440,14 @@ def TOFFOLI(self): [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 0], - ] + ], + dtype=self.dtype, ) def DEUTSCH(self, theta): sin = self.np.sin(theta) + 0j # 0j necessary for right tensorflow dtype - cos = self.np.cos(theta) - return self.np.array( + cos = self.np.cos(theta) + 0j + return self._cast( [ [1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], diff --git a/src/qibo/backends/tensorflow.py b/src/qibo/backends/tensorflow.py index cc2a9a9bb0..6490b37d9e 100644 --- a/src/qibo/backends/tensorflow.py +++ b/src/qibo/backends/tensorflow.py @@ -20,145 +20,11 @@ def __init__(self, dtype): self.tf = tf self.np = tnp - def RX(self, theta): - cos = self.np.cos(theta / 2.0) + 0j - isin = -1j * self.np.sin(theta / 2.0) - return self.tf.cast([[cos, isin], [isin, cos]], dtype=self.dtype) - - def RY(self, theta): - cos = self.np.cos(theta / 2.0) + 0j - sin = self.np.sin(theta / 2.0) + 0j - return self.tf.cast([[cos, -sin], [sin, cos]], dtype=self.dtype) - - def RZ(self, theta): - phase = self.np.exp(0.5j * theta) - return self.tf.cast([[self.np.conj(phase), 0], [0, phase]], dtype=self.dtype) - - def U1(self, theta): - phase = self.np.exp(1j * theta) - return self.tf.cast([[1, 0], [0, phase]], dtype=self.dtype) - - def U2(self, phi, lam): - eplus = self.np.exp(1j * (phi + lam) / 2.0) - eminus = self.np.exp(1j * (phi - lam) / 2.0) - return self.tf.cast( - [[self.np.conj(eplus), -self.np.conj(eminus)], [eminus, eplus]], - dtype=self.dtype, - ) / self.np.sqrt(2) - - def U3(self, theta, phi, lam): - cost = self.np.cos(theta / 2) - sint = self.np.sin(theta / 2) - eplus = self.np.exp(1j * (phi + lam) / 2.0) - eminus = self.np.exp(1j * (phi - lam) / 2.0) - return self.tf.cast( - [ - [self.np.conj(eplus) * cost, -self.np.conj(eminus) * sint], - [eminus * sint, eplus * cost], - ], - dtype=self.dtype, - ) - - def CRX(self, theta): - r = self.RX(theta) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, r[0, 0], r[0, 1]], - [0, 0, r[1, 0], r[1, 1]], - ], - dtype=self.dtype, - ) - - def CRY(self, theta): - r = self.RY(theta) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, r[0, 0], r[0, 1]], - [0, 0, r[1, 0], r[1, 1]], - ], - dtype=self.dtype, - ) - - def CRZ(self, theta): - r = self.RZ(theta) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, r[0, 0], r[0, 1]], - [0, 0, r[1, 0], r[1, 1]], - ], - dtype=self.dtype, - ) - - def CU1(self, theta): - r = self.U1(theta) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, r[0, 0], r[0, 1]], - [0, 0, r[1, 0], r[1, 1]], - ], - dtype=self.dtype, - ) - - def CU2(self, phi, lam): - r = self.U2(phi, lam) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, r[0, 0], r[0, 1]], - [0, 0, r[1, 0], r[1, 1]], - ], - dtype=self.dtype, - ) - - def CU3(self, theta, phi, lam): - r = self.U3(theta, phi, lam) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, r[0, 0], r[0, 1]], - [0, 0, r[1, 0], r[1, 1]], - ], - dtype=self.dtype, - ) - - def fSim(self, theta, phi): - cost = self.np.cos(theta) + 0j - isint = -1j * self.np.sin(theta) - phase = self.np.exp(-1j * phi) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, cost, isint, 0], - [0, isint, cost, 0], - [0, 0, 0, phase], - ], - dtype=self.dtype, - ) - - def GeneralizedfSim(self, u, phi): - phase = self.np.exp(-1j * phi) - return self.tf.cast( - [ - [1, 0, 0, 0], - [0, u[0, 0], u[0, 1], 0], - [0, u[1, 0], u[1, 1], 0], - [0, 0, 0, phase], - ], - dtype=self.dtype, - ) + def _cast(self, x, dtype): + return self.tf.cast(x, dtype=dtype) def Unitary(self, u): - return self.tf.cast(u, dtype=self.dtype) + return self._cast(u, dtype=self.dtype) class TensorflowBackend(NumpyBackend): diff --git a/src/qibo/callbacks.py b/src/qibo/callbacks.py index 16d88d712a..77114ed42a 100644 --- a/src/qibo/callbacks.py +++ b/src/qibo/callbacks.py @@ -125,7 +125,7 @@ def nqubits(self, n: int): ] def apply(self, backend, state): - from qibo.quantum_info.metrics import entanglement_entropy + from qibo.quantum_info.entropies import entanglement_entropy entropy, spectrum = entanglement_entropy( state, diff --git a/src/qibo/gates/abstract.py b/src/qibo/gates/abstract.py index d9f49f6a5e..a9b4ef0c2c 100644 --- a/src/qibo/gates/abstract.py +++ b/src/qibo/gates/abstract.py @@ -4,7 +4,7 @@ import sympy -from qibo.backends import CliffordBackend, GlobalBackend +from qibo.backends import _check_backend from qibo.config import raise_error REQUIRED_FIELDS = [ @@ -361,8 +361,7 @@ def matrix(self, backend=None): ``Gate.matrix`` was defined as an atribute in ``qibo`` versions prior to ``0.2.0``. From ``0.2.0`` on, it has been converted into a method and has replaced the ``asmatrix`` method. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) return backend.matrix(self) @@ -481,7 +480,6 @@ def substitute_symbols(self): self.parameters = tuple(params) def matrix(self, backend=None): - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) return backend.matrix_parametrized(self) diff --git a/src/qibo/gates/channels.py b/src/qibo/gates/channels.py index c620738da8..9616c59974 100644 --- a/src/qibo/gates/channels.py +++ b/src/qibo/gates/channels.py @@ -6,7 +6,7 @@ import numpy as np -from qibo.backends import GlobalBackend +from qibo.backends import _check_backend from qibo.config import PRECISION_TOL, raise_error from qibo.gates.abstract import Gate from qibo.gates.gates import I, Unitary, X, Y, Z @@ -80,8 +80,7 @@ def to_choi(self, nqubits: Optional[int] = None, order: str = "row", backend=Non vectorization, ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) nqubits = 1 + max(self.target_qubits) if nqubits is None else nqubits @@ -128,8 +127,7 @@ def to_liouville(self, nqubits: int = None, order: str = "row", backend=None): choi_to_liouville, ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) super_op = self.to_choi(nqubits=nqubits, order=order, backend=backend) super_op = choi_to_liouville(super_op, order=order, backend=backend) @@ -166,8 +164,7 @@ def to_pauli_liouville( from qibo.quantum_info.basis import comp_basis_to_pauli # pylint: disable=C0415 - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) super_op = self.to_liouville(nqubits=nqubits, backend=backend) diff --git a/src/qibo/gates/gates.py b/src/qibo/gates/gates.py index aa0e0d5ede..ae56a2723b 100644 --- a/src/qibo/gates/gates.py +++ b/src/qibo/gates/gates.py @@ -271,7 +271,11 @@ def decompose(self): being the :class:`qibo.gates.RX` gate. More precisely, :math:`\\sqrt{X} = e^{i \\pi / 4} \\, \\text{RX}(\\pi / 2)`. """ - return [RX(self.init_args[0], np.pi / 2, trainable=False)] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) def _dagger(self): """""" @@ -314,7 +318,11 @@ def decompose(self): being the :class:`qibo.gates.RX` gate. More precisely, :math:`(\\sqrt{X})^{\\dagger} = e^{-i \\pi / 4} \\, \\text{RX}(-\\pi / 2)`. """ - return [RX(self.init_args[0], -np.pi / 2, trainable=False)] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) def _dagger(self): """""" @@ -892,14 +900,11 @@ def decompose(self) -> List[Gate]: where :math:`\\text{RZ}` and :math:`\\sqrt{X}` are, respectively, :class:`qibo.gates.RZ` and :class`qibo.gates.SX`. """ - q = self.init_args[0] - return [ - RZ(q, self.init_kwargs["lam"]), - SX(q), - RZ(q, self.init_kwargs["theta"] + math.pi), - SX(q), - RZ(q, self.init_kwargs["phi"] + math.pi), - ] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) class U1q(_Un_): @@ -1020,8 +1025,11 @@ def decompose(self) -> List[Gate]: the target qubit, followed by :class:`qibo.gates.CNOT`, followed by a :class:`qibo.gates.S` in the target qubit. """ - q0, q1 = self.init_args - return [SDG(q1), CNOT(q0, q1), S(q1)] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) class CZ(Gate): @@ -1063,8 +1071,11 @@ def decompose(self) -> List[Gate]: the target qubit, followed by :class:`qibo.gates.CNOT`, followed by another :class:`qibo.gates.H` in the target qubit """ - q0, q1 = self.init_args - return [H(q1), CNOT(q0, q1), H(q1)] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) class CSX(Gate): @@ -1100,8 +1111,11 @@ def qasm_label(self): def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: """""" - q0, q1 = self.init_args - return [H(q1), CU1(q0, q1, np.pi / 2), H(q1)] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) def _dagger(self): """""" @@ -1141,8 +1155,11 @@ def qasm_label(self): def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: """""" - q0, q1 = self.init_args - return [H(q1), CU1(q0, q1, -np.pi / 2), H(q1)] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) def _dagger(self): """""" @@ -1508,7 +1525,7 @@ def qasm_label(self): class SiSWAP(Gate): - """The :math:`\\sqrt{\\text{iSWAP}}}` gate. + """The :math:`\\sqrt{\\text{iSWAP}}` gate. Corresponds to the following unitary matrix @@ -1533,6 +1550,39 @@ def __init__(self, q0, q1): self.init_args = [q0, q1] self.unitary = True + def _dagger(self) -> "Gate": + return SiSWAPDG(*self.qubits) + + +class SiSWAPDG(Gate): + """The :math:`\\left(\\sqrt{\\text{iSWAP}}\\right)^{\\dagger}` gate. + + Corresponds to the following unitary matrix + + .. math:: + \\begin{pmatrix} + 1 & 0 & 0 & 0 \\\\ + 0 & 1/\\sqrt{2} & -i/\\sqrt{2} & 0 \\\\ + 0 & -i/\\sqrt{2} & 1/\\sqrt{2} & 0 \\\\ + 0 & 0 & 0 & 1 \\\\ + \\end{pmatrix} + + Args: + q0 (int): the first qubit to be swapped id number. + q1 (int): the second qubit to be swapped id number. + """ + + def __init__(self, q0, q1): + super().__init__() + self.name = "siswapdg" + self.draw_label = "sidg" + self.target_qubits = (q0, q1) + self.init_args = [q0, q1] + self.unitary = True + + def _dagger(self) -> "Gate": + return SiSWAP(*self.qubits) + class FSWAP(Gate): """The fermionic swap gate. @@ -1868,9 +1918,11 @@ def __init__(self, q0, q1, theta, trainable=True): def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: """""" - q0, q1 = self.target_qubits - theta = self.init_kwargs["theta"] - return [H(q1), CNOT(q0, q1), RZ(q1, theta), CNOT(q0, q1), H(q1)] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) class RXXYY(_Rnn_): @@ -1909,22 +1961,11 @@ def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: the original gate due to a phase difference in :math:`\\left(\\sqrt{X}\\right)^{\\dagger}`. """ - q0, q1 = self.target_qubits - theta = self.init_kwargs["theta"] - return [ - RZ(q1, -np.pi / 2), - S(q0), - SX(q1), - RZ(q1, np.pi / 2), - CNOT(q1, q0), - RY(q0, -theta / 2), - RY(q1, -theta / 2), - CNOT(q1, q0), - SDG(q0), - RZ(q1, -np.pi / 2), - SX(q1).dagger(), - RZ(q1, np.pi / 2), - ] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) class MS(ParametrizedGate): @@ -2029,18 +2070,13 @@ def _dagger(self) -> "Gate": return self.__class__(*self.target_qubits, -self.parameters[0]) def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: - """Decomposition of Givens gate according to `ArXiv:2106.13839 - `_.""" - q0, q1 = self.target_qubits - theta = self.init_kwargs["theta"] - return [ - CNOT(q0, q1), - RY(q0, theta), - CNOT(q1, q0), - RY(q0, -theta), - CNOT(q1, q0), - CNOT(q0, q1), - ] + """Decomposition of RBS gate according to `ArXiv:2109.09685 + `_.""" + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) class RBS(ParametrizedGate): @@ -2090,18 +2126,11 @@ def _dagger(self) -> "Gate": def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: """Decomposition of RBS gate according to `ArXiv:2109.09685 `_.""" - q0, q1 = self.target_qubits - theta = self.init_kwargs["theta"] - return [ - H(q0), - CNOT(q0, q1), - H(q1), - RY(q0, theta), - RY(q1, -theta), - H(q1), - CNOT(q0, q1), - H(q0), - ] + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) + + return standard_decompositions(self) class ECR(Gate): @@ -2142,9 +2171,11 @@ def decompose(self, *free, use_toffolis: bool = True) -> List[Gate]: \\textup{ECR} = e^{i 7 \\pi / 4} \\, S(q_{0}) \\, \\sqrt{X}(q_{1}) \\, \\textup{CNOT}(q_{0}, q_{1}) \\, X(q_{0}) """ + from qibo.transpiler.decompositions import ( # pylint: disable=C0415 + standard_decompositions, + ) - q0, q1 = self.target_qubits - return [S(q0), SX(q1), CNOT(q0, q1), X(q0)] + return standard_decompositions(self) class TOFFOLI(Gate): diff --git a/src/qibo/gates/special.py b/src/qibo/gates/special.py index 5243b9fa52..3824f3672c 100644 --- a/src/qibo/gates/special.py +++ b/src/qibo/gates/special.py @@ -1,4 +1,4 @@ -from qibo.backends import GlobalBackend +from qibo.backends import _check_backend from qibo.gates.abstract import SpecialGate from qibo.gates.measurements import M @@ -104,8 +104,7 @@ def matrix(self, backend=None): Returns: ndarray: Matrix representation of special gate. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) return backend.matrix_fused(self) diff --git a/src/qibo/hamiltonians/hamiltonians.py b/src/qibo/hamiltonians/hamiltonians.py index 6354bf316b..10b9419580 100644 --- a/src/qibo/hamiltonians/hamiltonians.py +++ b/src/qibo/hamiltonians/hamiltonians.py @@ -24,14 +24,9 @@ class Hamiltonian(AbstractHamiltonian): """ def __init__(self, nqubits, matrix=None, backend=None): - if backend is None: # pragma: no cover - from qibo.backends import ( # pylint: disable=import-outside-toplevel - GlobalBackend, - ) + from qibo.backends import _check_backend - self.backend = GlobalBackend() - else: - self.backend = backend + self.backend = _check_backend(backend) if not ( isinstance(matrix, self.backend.tensor_types) @@ -355,14 +350,11 @@ def __init__(self, form=None, nqubits=None, symbol_map={}, backend=None): from qibo.symbols import Symbol # pylint: disable=import-outside-toplevel self._qiboSymbol = Symbol # also used in ``self._get_symbol_matrix`` - if backend is None: # pragma: no cover - from qibo.backends import ( # pylint: disable=import-outside-toplevel - GlobalBackend, - ) - self.backend = GlobalBackend() - else: - self.backend = backend + from qibo.backends import _check_backend + + self.backend = _check_backend(backend) + if form is not None: self.form = form if nqubits is not None: diff --git a/src/qibo/models/circuit.py b/src/qibo/models/circuit.py index 419f5635d2..e15223f9ef 100644 --- a/src/qibo/models/circuit.py +++ b/src/qibo/models/circuit.py @@ -1000,14 +1000,13 @@ def fuse(self, max_qubits=2): def unitary(self, backend=None): """Creates the unitary matrix corresponding to all circuit gates. - This is a ``(2 ** nqubits, 2 ** nqubits)`` matrix obtained by - multiplying all circuit gates. + This is a :math:`2^{n} \\times 2^{n}`` matrix obtained by + multiplying all circuit gates, where :math:`n` is ``nqubits``. """ - if backend is None: - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend - backend = GlobalBackend() + backend = _check_backend(backend) fgate = gates.FusedGate(*range(self.nqubits)) for gate in self.queue: @@ -1050,10 +1049,10 @@ def compile(self, backend=None): NotImplementedError, "Circuit compilation is not available with callbacks.", ) - if backend is None: - from qibo.backends import GlobalBackend - backend = GlobalBackend() + from qibo.backends import _check_backend + + backend = _check_backend(backend) from qibo.result import CircuitResult, QuantumState diff --git a/src/qibo/models/encodings.py b/src/qibo/models/encodings.py index a85024ad3c..8d37bb8922 100644 --- a/src/qibo/models/encodings.py +++ b/src/qibo/models/encodings.py @@ -70,28 +70,52 @@ def comp_basis_encoder( return circuit -def unary_encoder(data, architecture: str = "tree"): - """Creates circuit that performs the unary encoding of ``data``. +def phase_encoder(data, rotation: str = "RY"): + """Creates circuit that performs the phase encoding of ``data``. - Given a classical ``data`` array :math:`\\mathbf{x} \\in \\mathbb{R}^{d}` such that + Args: + data (ndarray or list): :math:`1`-dimensional array of phases to be loaded. + rotation (str, optional): If ``"RX"``, uses :class:`qibo.gates.gates.RX` as rotation. + If ``"RY"``, uses :class:`qibo.gates.gates.RY` as rotation. + If ``"RZ"``, uses :class:`qibo.gates.gates.RZ` as rotation. + Defaults to ``"RY"``. - .. math:: - \\mathbf{x} = (x_{1}, x_{2}, \\dots, x_{d}) \\, , + Returns: + :class:`qibo.models.circuit.Circuit`: circuit that loads ``data`` in phase encoding. + """ + if isinstance(data, list): + data = np.array(data) + + if len(data.shape) != 1: + raise_error( + TypeError, + f"``data`` must be a 1-dimensional array, but it has dimensions {data.shape}.", + ) + + if not isinstance(rotation, str): + raise_error( + TypeError, + f"``rotation`` must be type str, but it is type {type(rotation)}.", + ) - this function generate the circuit that prepares the following quantum state - :math:`\\ket{\\psi} \\in \\mathcal{H}`: + if rotation not in ["RX", "RY", "RZ"]: + raise_error(ValueError, f"``rotation`` {rotation} not found.") + + nqubits = len(data) + gate = getattr(gates, rotation.upper()) + + circuit = Circuit(nqubits) + circuit.add(gate(qubit, 0.0) for qubit in range(nqubits)) + circuit.set_parameters(data) + + return circuit - .. math:: - \\ket{\\psi} = \\frac{1}{\\|\\mathbf{x}\\|_{\\textup{HS}}} \\, - \\sum_{k=1}^{d} \\, x_{k} \\, \\ket{k} \\, , - with :math:`\\mathcal{H} \\cong \\mathbb{C}^{d}` being a :math:`d`-qubit Hilbert space, - and :math:`\\|\\cdot\\|_{\\textup{HS}}` being the Hilbert-Schmidt norm. - Here, :math:`\\ket{k}` is a unary representation of the number :math:`1` through - :math:`d`. +def unary_encoder(data, architecture: str = "tree"): + """Creates circuit that performs the (deterministic) unary encoding of ``data``. Args: - data (ndarray, optional): :math:`1`-dimensional array of data to be loaded. + data (ndarray): :math:`1`-dimensional array of data to be loaded. architecture(str, optional): circuit architecture used for the unary loader. If ``diagonal``, uses a ladder-like structure. If ``tree``, uses a binary-tree-based structure. @@ -99,11 +123,10 @@ def unary_encoder(data, architecture: str = "tree"): Returns: :class:`qibo.models.circuit.Circuit`: circuit that loads ``data`` in unary representation. - - References: - 1. S. Johri *et al.*, *Nearest Centroid Classification on a Trapped Ion Quantum Computer*. - `arXiv:2012.04145v2 [quant-ph] `_. """ + if isinstance(data, list): + data = np.array(data) + if len(data.shape) != 1: raise_error( TypeError, @@ -143,26 +166,12 @@ def unary_encoder(data, architecture: str = "tree"): def unary_encoder_random_gaussian(nqubits: int, architecture: str = "tree", seed=None): """Creates a circuit that performs the unary encoding of a random Gaussian state. - Given :math:`d` qubits, encodes the quantum state - :math:`\\ket{\\psi} \\in \\mathcal{H}` such that - - - .. math:: - \\ket{\\psi} = \\frac{1}{\\|\\mathbf{x}\\|_{\\textup{HS}}} \\, - \\sum_{k=1}^{d} \\, x_{k} \\, \\ket{k} - - where :math:`x_{k}` are independent Gaussian random variables, - :math:`\\mathcal{H} \\cong \\mathbb{C}^{d}` is a :math:`d`-qubit Hilbert space, - and :math:`\\|\\cdot\\|_{\\textup{HS}}` being the Hilbert-Schmidt norm. - Here, :math:`\\ket{k}` is a unary representation of the number :math:`1` through - :math:`d`. - - At depth :math:`h`, the angles :math:`\\theta_{k} \\in [0, 2\\pi]` of the the + At depth :math:`h` of the tree architecture, the angles :math:`\\theta_{k} \\in [0, 2\\pi]` of the the gates :math:`RBS(\\theta_{k})` are sampled from the following probability density function: .. math:: - p_{h}(\\theta) = \\frac{1}{2} \\, \\frac{\\Gamma(2^{h-1})}{\\Gamma^{2}(2^{h-2})} - \\abs{\\sin(\\theta) \\, \\cos(\\theta)}^{2^{h-1} - 1} \\, , + p_{h}(\\theta) = \\frac{1}{2} \\, \\frac{\\Gamma(2^{h-1})}{\\Gamma^{2}(2^{h-2})} \\, + \\left|\\sin(\\theta) \\, \\cos(\\theta)\\right|^{2^{h-1} - 1} \\, , where :math:`\\Gamma(\\cdot)` is the `Gamma function `_. diff --git a/src/qibo/models/error_mitigation.py b/src/qibo/models/error_mitigation.py index 2e2d577d2e..9af448c564 100644 --- a/src/qibo/models/error_mitigation.py +++ b/src/qibo/models/error_mitigation.py @@ -6,7 +6,7 @@ from scipy.optimize import curve_fit from qibo import gates -from qibo.backends import GlobalBackend +from qibo.backends import GlobalBackend, _check_backend from qibo.config import raise_error @@ -148,8 +148,8 @@ def ZNE( 1. K. Temme, S. Bravyi et al, *Error mitigation for short-depth quantum circuits*. `arXiv:1612.02058 [quant-ph] `_. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) + if readout is None: readout = {} @@ -197,8 +197,7 @@ def sample_training_circuit_cdr( Returns: :class:`qibo.models.Circuit`: The sampled circuit. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if replacement_gates is None: replacement_gates = [(gates.RZ, {"theta": n * np.pi / 2}) for n in range(4)] @@ -303,8 +302,8 @@ def CDR( 1. P. Czarnik, A. Arrasmith et al, *Error mitigation with Clifford quantum-circuit data*. `arXiv:2005.10189 [quant-ph] `_. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) + if readout is None: readout = {} @@ -314,7 +313,8 @@ def CDR( train_val = {"noise-free": [], "noisy": []} for circ in training_circuits: - val = circ(nshots=nshots).expectation_from_samples(observable) + result = backend.execute_circuit(circ, nshots=nshots) + val = result.expectation_from_samples(observable) train_val["noise-free"].append(val) val = get_expectation_val_with_readout_mitigation( circ, observable, noise_model, nshots, readout, qubit_map, backend=backend @@ -389,8 +389,8 @@ def vnCDR( 1. A. Lowe, MH. Gordon et al, *Unified approach to data-driven quantum error mitigation*. `arXiv:2011.01157 [quant-ph] `_. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) + if readout is None: readout = {} @@ -400,7 +400,8 @@ def vnCDR( train_val = {"noise-free": [], "noisy": []} for circ in training_circuits: - val = circ(nshots=nshots).expectation_from_samples(observable) + result = backend.execute_circuit(circ, nshots=nshots) + val = result.expectation_from_samples(observable) train_val["noise-free"].append(val) for level in noise_levels: noisy_c = get_noisy_circuit(circ, level, insertion_gate=insertion_gate) @@ -493,8 +494,7 @@ def get_response_matrix( """ from qibo import Circuit # pylint: disable=import-outside-toplevel - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) response_matrix = np.zeros((2**nqubits, 2**nqubits)) @@ -597,8 +597,7 @@ def apply_randomized_readout_mitigation( random_pauli, ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) meas_qubits = circuit.measurements[0].qubits nshots_r = int(nshots / ncircuits) @@ -670,8 +669,8 @@ def get_expectation_val_with_readout_mitigation( Returns: float: the mitigated expectation value of the observable. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) + if readout is None: # pragma: no cover readout = {} @@ -717,8 +716,7 @@ def sample_clifford_training_circuit( random_clifford, ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) non_clifford_gates_indices = [ i @@ -781,8 +779,7 @@ def error_sensitive_circuit(circuit, observable, backend=None): vectorization, ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) sampled_circuit = sample_clifford_training_circuit(circuit, backend=backend) unitary_matrix = sampled_circuit.unitary(backend=backend) @@ -880,8 +877,8 @@ def ICS( 1. Dayue Qin, Yanzhu Chen et al, *Error statistics and scalability of quantum error mitigation formulas*. `arXiv:2112.06255 [quant-ph] `_. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) + if readout is None: readout = {} @@ -897,7 +894,7 @@ def ICS( lambda_list = [] for training_circuit in training_circuits: - circuit_result = training_circuit(nshots=nshots) + circuit_result = backend.execute_circuit(training_circuit, nshots=nshots) expectation = observable.expectation_from_samples(circuit_result.frequencies()) noisy_expectation = get_expectation_val_with_readout_mitigation( diff --git a/src/qibo/models/grover.py b/src/qibo/models/grover.py index 2da268aa97..c18fab512e 100644 --- a/src/qibo/models/grover.py +++ b/src/qibo/models/grover.py @@ -188,10 +188,9 @@ def iterative_grover(self, lamda_value=6 / 5, backend=None): measured (str): bitstring measured and checked as a valid solution. total_iterations (int): number of times the oracle has been called. """ - if backend is None: # pragma: no cover - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend - backend = GlobalBackend() + backend = _check_backend(backend) k = 1 lamda = lamda_value @@ -225,10 +224,9 @@ def execute(self, nshots=100, freq=False, logs=False, backend=None): solution (str): bitstring (or list of bitstrings) measured as solution of the search. iterations (int): number of oracle calls done to reach a solution. """ - if backend is None: # pragma: no cover - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend - backend = GlobalBackend() + backend = _check_backend(backend) if (self.num_sol or self.targ_a) and not self.iterative: if self.targ_a: diff --git a/src/qibo/models/hep.py b/src/qibo/models/hep.py index 13466cab4e..e03429136e 100644 --- a/src/qibo/models/hep.py +++ b/src/qibo/models/hep.py @@ -41,12 +41,9 @@ def __init__(self, ansatz, layers, nqubits, multi_output=False, backend=None): self.circuit, self.rotation, self.nparams = ansatz_function(layers, nqubits) # load backend - if backend is None: # pragma: no cover - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend - self.backend = GlobalBackend() - else: - self.backend = backend + self.backend = _check_backend(backend) # load hamiltonian if multi_output: diff --git a/src/qibo/models/iqae.py b/src/qibo/models/iqae.py index 081c787a1a..caf28e72b7 100644 --- a/src/qibo/models/iqae.py +++ b/src/qibo/models/iqae.py @@ -264,10 +264,10 @@ def execute(self, backend=None): Returns: A :class:`qibo.models.iqae.IterativeAmplitudeEstimationResult` results object. """ - if backend is None: - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend + + backend = _check_backend(backend) - backend = GlobalBackend() # Initializing all parameters k = [0] # uppercase_k=4k+2 diff --git a/src/qibo/models/qcnn.py b/src/qibo/models/qcnn.py index 5d569facac..f56ed804cf 100644 --- a/src/qibo/models/qcnn.py +++ b/src/qibo/models/qcnn.py @@ -1,6 +1,7 @@ import numpy as np -from qibo import Circuit, gates +from qibo import gates, get_backend +from qibo.models import Circuit class QuantumCNN: @@ -16,14 +17,16 @@ class QuantumCNN: nclasses (int): number of classes to be classified. Default setting of 2 (phases). params: initial list of variational parameters. If not provided, all parameters will be initialized to zero. + twoqubitansatz (:class:`qibo.models.Circuit`): a two qubit ansatz that can be input by the user to form the two qubit ansatz used in the convolutional circuit. Example: .. testcode:: - import qibo - from qibo.models.qcnn import QuantumCNN + import math - import qibo import numpy as np import random + import qibo + from qibo.models.qcnn import QuantumCNN + qibo.set_backend("numpy") data = np.random.rand(16) @@ -42,12 +45,31 @@ class QuantumCNN: """ - def __init__(self, nqubits, nlayers, nclasses=2, params=None): + def __init__( + self, + nqubits, + nlayers, + nclasses=2, + params=None, + twoqubitansatz=None, + copy_init_state=None, + ): self.nclasses = nclasses self.nqubits = nqubits self.nlayers = nlayers + self.twoqubitansatz = twoqubitansatz + + if copy_init_state is None: + if "qibojit" in get_backend(): + self.copy_init_state = True + else: + self.copy_init_state = False + + if self.twoqubitansatz == None: + self.nparams_conv = 15 + else: + self.nparams_conv = len(self.twoqubitansatz.get_parameters()) - self.nparams_conv = 15 self.nparams_pool = 6 self.nparams_layer = self.nparams_conv + self.nparams_pool self.measured_qubits = int(np.ceil(np.log2(self.nclasses))) @@ -161,15 +183,21 @@ def two_qubit_unitary(self, bits, symbols): Circuit containing the unitaries added to the specified qubits. """ - c = Circuit(self.nqubits) - c += self.one_qubit_unitary(bits[0], symbols[0:3]) - c += self.one_qubit_unitary(bits[1], symbols[3:6]) - c.add(gates.RZZ(bits[0], bits[1], symbols[6])) - c.add(gates.RYY(bits[0], bits[1], symbols[7])) - c.add(gates.RXX(bits[0], bits[1], symbols[8])) + if self.twoqubitansatz is None: + c = Circuit(self.nqubits) + c += self.one_qubit_unitary(bits[0], symbols[0:3]) + c += self.one_qubit_unitary(bits[1], symbols[3:6]) + c.add(gates.RZZ(bits[0], bits[1], symbols[6])) + c.add(gates.RYY(bits[0], bits[1], symbols[7])) + c.add(gates.RXX(bits[0], bits[1], symbols[8])) - c += self.one_qubit_unitary(bits[0], symbols[9:12]) - c += self.one_qubit_unitary(bits[1], symbols[12:]) + c += self.one_qubit_unitary(bits[0], symbols[9:12]) + c += self.one_qubit_unitary(bits[1], symbols[12:]) + + else: + c = Circuit(self.nqubits) + c.add(self.twoqubitansatz.on_qubits(bits[0], bits[1])) + c.set_parameters(symbols[0 : self.nparams_conv]) return c @@ -251,7 +279,11 @@ def Predictions(self, circuit, theta, init_state, nshots=10000): numpy.array() with predictions for each qubit, for the initial state. """ bias = np.array(theta[0 : self.measured_qubits]) - circuit_exec = circuit(init_state, nshots) + if self.copy_init_state: + init_state_copy = init_state.copy() + else: + init_state_copy = init_state + circuit_exec = circuit(init_state_copy, nshots) result = circuit_exec.frequencies(binary=False) prediction = np.zeros(self.measured_qubits) @@ -309,17 +341,15 @@ def minimize( data: the training data to be used in the minimization. labels: the corresponding ground truth for the training data. nshots: int number of runs of the circuit during the sampling process (default=10000). - method: str 'classical optimizer for the minimization'. All methods from scipy.optimize.minmize are suported (default='Powell'). + method: str 'classical optimizer for the minimization'. All methods from qibo.optimizers.optimize are suported (default='Powell'). Returns: numpy.float64 with value of the minimum found, numpy.ndarray with the optimal angles. """ - from scipy.optimize import minimize + from qibo.optimizers import optimize - result = minimize( + loss, optimal_angles, result = optimize( self.Cost_function, init_theta, args=(data, labels, nshots), method=method ) - loss = result.fun - optimal_angles = result.x self._optimal_angles = optimal_angles diff --git a/src/qibo/models/tsp.py b/src/qibo/models/tsp.py index 3db11f818e..250360aa84 100644 --- a/src/qibo/models/tsp.py +++ b/src/qibo/models/tsp.py @@ -135,12 +135,10 @@ def qaoa_function_of_layer(layer, distance_matrix): """ def __init__(self, distance_matrix, backend=None): - if backend is None: # pragma: no cover - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend + + self.backend = _check_backend(backend) - self.backend = GlobalBackend() - else: - self.backend = backend self.distance_matrix = distance_matrix self.num_cities = distance_matrix.shape[0] self.two_to_one = calculate_two_to_one(self.num_cities) diff --git a/src/qibo/noise_model.py b/src/qibo/noise_model.py index 892eb48743..44311c2661 100644 --- a/src/qibo/noise_model.py +++ b/src/qibo/noise_model.py @@ -341,10 +341,9 @@ def fit( from scipy.optimize import Bounds, direct - if backend == None: # pragma: no cover - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend - backend = GlobalBackend() + backend = _check_backend(backend) nshots = target_result.nshots target_prob = freq_to_prob(target_result.frequencies()) diff --git a/src/qibo/optimizers.py b/src/qibo/optimizers.py index 92eed2fbeb..0b5c46c778 100644 --- a/src/qibo/optimizers.py +++ b/src/qibo/optimizers.py @@ -86,16 +86,16 @@ def myloss(parameters, circuit): ) return cmaes(loss, initial_parameters, args, options) elif method == "sgd": - if backend is None: - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend + + backend = _check_backend(backend) - backend = GlobalBackend() return sgd(loss, initial_parameters, args, options, compile, backend) else: - if backend is None: - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend + + backend = _check_backend(backend) - backend = GlobalBackend() return newtonian( loss, initial_parameters, diff --git a/src/qibo/parallel.py b/src/qibo/parallel.py index 0bbf84f055..22a65fafe7 100644 --- a/src/qibo/parallel.py +++ b/src/qibo/parallel.py @@ -6,7 +6,7 @@ from joblib import Parallel, delayed -from qibo.backends import GlobalBackend, set_threads +from qibo.backends import _check_backend from qibo.config import raise_error @@ -39,8 +39,7 @@ def parallel_execution(circuit, states, processes=None, backend=None): Returns: Circuit evaluation for input states. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if states is None or not isinstance(states, list): # pragma: no cover raise_error(TypeError, "states must be a list.") @@ -88,8 +87,7 @@ def parallel_circuits_execution( Returns: Circuit evaluation for input states. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if not isinstance(circuits, Iterable): # pragma: no cover raise_error(TypeError, "circuits must be iterable.") @@ -165,8 +163,7 @@ def parallel_parametrized_execution( Returns: Circuit evaluation for input parameters. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if not isinstance(parameters, list): # pragma: no cover raise_error(TypeError, "parameters must be a list.") diff --git a/src/qibo/quantum_info/__init__.py b/src/qibo/quantum_info/__init__.py index 27e3edb046..8d9400104e 100644 --- a/src/qibo/quantum_info/__init__.py +++ b/src/qibo/quantum_info/__init__.py @@ -1,5 +1,7 @@ from qibo.quantum_info.basis import * from qibo.quantum_info.clifford import * +from qibo.quantum_info.entanglement import * +from qibo.quantum_info.entropies import * from qibo.quantum_info.metrics import * from qibo.quantum_info.quantum_networks import * from qibo.quantum_info.random_ensembles import * diff --git a/src/qibo/quantum_info/basis.py b/src/qibo/quantum_info/basis.py index 119cf78380..dcb5d7ab84 100644 --- a/src/qibo/quantum_info/basis.py +++ b/src/qibo/quantum_info/basis.py @@ -5,7 +5,7 @@ import numpy as np from qibo import matrices -from qibo.backends import GlobalBackend +from qibo.backends import _check_backend from qibo.config import raise_error from qibo.quantum_info.superoperator_transformations import vectorization @@ -89,8 +89,7 @@ def pauli_basis( "sparse representation is not implemented for unvectorized Pauli basis.", ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) pauli_labels = {"I": matrices.I, "X": matrices.X, "Y": matrices.Y, "Z": matrices.Z} basis_single = [pauli_labels[label] for label in pauli_order] @@ -187,8 +186,7 @@ def comp_basis_to_pauli( array with their row-wise indexes. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if sparse: elements, indexes = pauli_basis( @@ -258,8 +256,7 @@ def pauli_to_comp_basis( tuple is composed of array of non-zero elements and an array with their row-wise indexes. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) unitary = pauli_basis( nqubits, diff --git a/src/qibo/quantum_info/entanglement.py b/src/qibo/quantum_info/entanglement.py new file mode 100644 index 0000000000..28cbb70305 --- /dev/null +++ b/src/qibo/quantum_info/entanglement.py @@ -0,0 +1,293 @@ +"""Submodules with entanglement measures.""" + +import numpy as np + +from qibo.backends import _check_backend +from qibo.config import PRECISION_TOL, raise_error +from qibo.quantum_info.metrics import fidelity, purity + + +def concurrence(state, bipartition, check_purity: bool = True, backend=None): + """Calculates concurrence of a pure bipartite quantum state + :math:`\\rho \\in \\mathcal{H}_{A} \\otimes \\mathcal{H}_{B}` as + + .. math:: + C(\\rho) = \\sqrt{2 \\, (\\text{tr}^{2}(\\rho) - \\text{tr}(\\rho_{A}^{2}))} \\, , + + where :math:`\\rho_{A} = \\text{tr}_{B}(\\rho)` is the reduced density operator + obtained by tracing out the qubits in the ``bipartition`` :math:`B`. + + Args: + state (ndarray): statevector or density matrix. + bipartition (list or tuple or ndarray): qubits in the subsystem to be traced out. + check_purity (bool, optional): if ``True``, checks if ``state`` is pure. If ``False``, + it assumes ``state`` is pure . Defaults to ``True``. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. + Defaults to ``None``. + + Returns: + float: Concurrence of :math:`\\rho`. + """ + backend = _check_backend(backend) + + if ( + (len(state.shape) not in [1, 2]) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + if not isinstance(check_purity, bool): + raise_error( + TypeError, + f"check_purity must be type bool, but it is type {type(check_purity)}.", + ) + + nqubits = int(np.log2(state.shape[0])) + + if check_purity is True: + purity_total_system = purity(state) + + mixed = bool(abs(purity_total_system - 1.0) > PRECISION_TOL) + if mixed is True: + raise_error( + NotImplementedError, + "concurrence only implemented for pure quantum states.", + ) + + reduced_density_matrix = ( + backend.partial_trace(state, bipartition, nqubits) + if len(state.shape) == 1 + else backend.partial_trace_density_matrix(state, bipartition, nqubits) + ) + + purity_reduced = purity(reduced_density_matrix) + if purity_reduced - 1.0 > 0.0: + purity_reduced = round(purity_reduced, 7) + + concur = np.sqrt(2 * (1 - purity_reduced)) + + return concur + + +def entanglement_of_formation( + state, bipartition, base: float = 2, check_purity: bool = True, backend=None +): + """Calculates the entanglement of formation :math:`E_{f}` of a pure bipartite + quantum state :math:`\\rho`, which is given by + + .. math:: + E_{f} = H([1 - x, x]) \\, , + + where + + .. math:: + x = \\frac{1 + \\sqrt{1 - C^{2}(\\rho)}}{2} \\, , + + :math:`C(\\rho)` is the :func:`qibo.quantum_info.concurrence` of :math:`\\rho`, + and :math:`H` is the :func:`qibo.quantum_info.entropies.shannon_entropy`. + + Args: + state (ndarray): statevector or density matrix. + bipartition (list or tuple or ndarray): qubits in the subsystem to be traced out. + base (float): the base of the log in :func:`qibo.quantum_info.entropies.shannon_entropy`. + Defaults to :math:`2`. + check_purity (bool, optional): if ``True``, checks if ``state`` is pure. If ``False``, + it assumes ``state`` is pure . Default: ``True``. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. + Defaults to ``None``. + + + Returns: + float: entanglement of formation of state :math:`\\rho`. + """ + from qibo.quantum_info.entropies import shannon_entropy # pylint: disable=C0415 + + backend = _check_backend(backend) + + concur = concurrence( + state, bipartition=bipartition, check_purity=check_purity, backend=backend + ) + concur = (1 + np.sqrt(1 - concur**2)) / 2 + probabilities = [1 - concur, concur] + + ent_of_form = shannon_entropy(probabilities, base=base, backend=backend) + + return ent_of_form + + +def entanglement_fidelity( + channel, nqubits: int, state=None, check_hermitian: bool = False, backend=None +): + """Entanglement fidelity :math:`F_{\\mathcal{E}}` of a ``channel`` :math:`\\mathcal{E}` + on ``state`` :math:`\\rho` is given by + + .. math:: + F_{\\mathcal{E}}(\\rho) = F(\\rho_{f}, \\rho) + + where :math:`F` is the :func:`qibo.quantum_info.fidelity` function for states, + and :math:`\\rho_{f} = \\mathcal{E}_{A} \\otimes I_{B}(\\rho)` + is the state after the channel :math:`\\mathcal{E}` was applied to + partition :math:`A`. + + Args: + channel (:class:`qibo.gates.channels.Channel`): quantum channel + acting on partition :math:`A`. + nqubits (int): total number of qubits in ``state``. + state (ndarray, optional): statevector or density matrix to be evolved + by ``channel``. If ``None``, defaults to the maximally entangled state + :math:`\\frac{1}{2^{n}} \\, \\sum_{k} \\, \\ket{k}\\ket{k}`, where + :math:`n` is ``nqubits``. Defaults to ``None``. + check_hermitian (bool, optional): if ``True``, checks if the final state + :math:`\\rho_{f}` is Hermitian. If ``False``, it assumes it is Hermitian. + Defaults to ``False``. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. + Defaults to ``None``. + + Returns: + float: Entanglement fidelity :math:`F_{\\mathcal{E}}`. + """ + if not isinstance(nqubits, int): + raise_error( + TypeError, f"nqubits must be type int, but it is type {type(nqubits)}." + ) + + if nqubits <= 0: + raise_error( + ValueError, f"nqubits must be a positive integer, but it is {nqubits}." + ) + + if state is not None and ( + (len(state.shape) not in [1, 2]) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + if not isinstance(check_hermitian, bool): + raise_error( + TypeError, + f"check_hermitian must be type bool, but it is type {type(check_hermitian)}.", + ) + + backend = _check_backend(backend) + + if state is None: + state = backend.plus_density_matrix(nqubits) + + # necessary because this function do support repeated execution, + # so it has to default to density matrices + if len(state.shape) == 1: + state = np.outer(state, np.conj(state)) + + state_final = backend.apply_channel_density_matrix(channel, state, nqubits) + + entang_fidelity = fidelity( + state_final, state, check_hermitian=check_hermitian, backend=backend + ) + + return entang_fidelity + + +def meyer_wallach_entanglement(circuit, backend=None): + """Computes the Meyer-Wallach entanglement Q of the `circuit`, + + .. math:: + Q(\\theta) = 1 - \\frac{1}{N} \\, \\sum_{k} \\, + \\text{tr}\\left(\\rho_{k^{2}}(\\theta)\\right) \\, . + + Args: + circuit (:class:`qibo.models.Circuit`): Parametrized circuit. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. + Defaults to ``None``. + + Returns: + float: Meyer-Wallach entanglement. + """ + + backend = _check_backend(backend) + + circuit.density_matrix = True + nqubits = circuit.nqubits + + rho = backend.execute_circuit(circuit).state() + + ent = 0 + for j in range(nqubits): + trace_q = list(range(nqubits)) + trace_q.pop(j) + + rho_r = backend.partial_trace_density_matrix(rho, trace_q, nqubits) + + trace = purity(rho_r) + + ent += trace + + entanglement = 1 - ent / nqubits + + return entanglement + + +def entangling_capability(circuit, samples: int, seed=None, backend=None): + """Returns the entangling capability :math:`\\text{Ent}` of a parametrized + circuit, which is average Meyer-Wallach entanglement Q of the circuit, i.e. + + .. math:: + \\text{Ent} = \\frac{2}{S}\\sum_{k}Q_k \\, , + + where :math:`S` is the number of samples. + + Args: + circuit (:class:`qibo.models.Circuit`): Parametrized circuit. + samples (int): number of samples to estimate the integral. + seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random + numbers or a fixed seed to initialize a generator. If ``None``, initializes + a generator with a random seed. Default: ``None``. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. + Defaults to ``None``. + + Returns: + float: Entangling capability. + """ + + if not isinstance(samples, int): + raise_error( + TypeError, f"samples must be type int, but it is type {type(samples)}." + ) + + if ( + seed is not None + and not isinstance(seed, int) + and not isinstance(seed, np.random.Generator) + ): + raise_error( + TypeError, "seed must be either type int or numpy.random.Generator." + ) + + backend = _check_backend(backend) + + local_state = ( + np.random.default_rng(seed) if seed is None or isinstance(seed, int) else seed + ) + + res = [] + for _ in range(samples): + params = local_state.uniform(-np.pi, np.pi, circuit.trainable_gates.nparams) + circuit.set_parameters(params) + entanglement = meyer_wallach_entanglement(circuit, backend=backend) + res.append(entanglement) + + capability = 2 * np.real(np.sum(res)) / samples + + return capability diff --git a/src/qibo/quantum_info/entropies.py b/src/qibo/quantum_info/entropies.py new file mode 100644 index 0000000000..716bdda3fd --- /dev/null +++ b/src/qibo/quantum_info/entropies.py @@ -0,0 +1,876 @@ +"""Submodule with entropy measures.""" + +from typing import Union + +import numpy as np +from scipy.linalg import fractional_matrix_power + +from qibo.backends import _check_backend +from qibo.config import PRECISION_TOL, raise_error +from qibo.quantum_info.metrics import _check_hermitian_or_not_gpu, purity + + +def shannon_entropy(prob_dist, base: float = 2, backend=None): + """Calculate the Shannon entropy of a probability array :math:`\\mathbf{p}`, which is given by + + .. math:: + H(\\mathbf{p}) = - \\sum_{k = 0}^{d^{2} - 1} \\, p_{k} \\, \\log_{b}(p_{k}) \\, , + + where :math:`d = \\text{dim}(\\mathcal{H})` is the dimension of the + Hilbert space :math:`\\mathcal{H}`, :math:`b` is the log base (default 2), + and :math:`0 \\log_{b}(0) \\equiv 0`. + + Args: + prob_dist (ndarray or list): a probability array :math:`\\mathbf{p}`. + base (float): the base of the log. Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. + Defaults to ``None``. + + Returns: + (float): Shannon entropy :math:`H(\\mathcal{p})`. + """ + backend = _check_backend(backend) + + if isinstance(prob_dist, list): + # np.float64 is necessary instead of native float because of tensorflow + prob_dist = backend.cast(prob_dist, dtype=np.float64) + + if base <= 0: + raise_error(ValueError, "log base must be non-negative.") + + if len(prob_dist.shape) != 1: + raise_error( + TypeError, + f"Probability array must have dims (k,) but it has {prob_dist.shape}.", + ) + + if len(prob_dist) == 0: + raise_error(TypeError, "Empty array.") + + if any(prob_dist < 0) or any(prob_dist > 1.0): + raise_error( + ValueError, + "All elements of the probability array must be between 0. and 1..", + ) + + if np.abs(np.sum(prob_dist) - 1.0) > PRECISION_TOL: + raise_error(ValueError, "Probability array must sum to 1.") + + log_prob = np.where(prob_dist != 0, np.log2(prob_dist) / np.log2(base), 0.0) + + shan_entropy = -np.sum(prob_dist * log_prob) + + # absolute value if entropy == 0.0 to avoid returning -0.0 + shan_entropy = np.abs(shan_entropy) if shan_entropy == 0.0 else shan_entropy + + return complex(shan_entropy).real + + +def classical_relative_entropy(prob_dist_p, prob_dist_q, base: float = 2, backend=None): + """Calculates the relative entropy between two discrete probability distributions. + + For probabilities :math:`\\mathbf{p}` and :math:`\\mathbf{q}`, it is defined as + + ..math:: + D(\\mathbf{p} \\, \\| \\, \\mathbf{q}) = \\sum_{x} \\, \\mathbf{p}(x) \\, + \\log\\left( \\frac{\\mathbf{p}(x)}{\\mathbf{q}(x)} \\right) \\, . + + The classical relative entropy is also known as the + `Kullback-Leibler (KL) divergence `_. + + Args: + prob_dist_p (ndarray or list): discrete probability distribution :math:`p`. + prob_dist_q (ndarray or list): discrete probability distribution :math:`q`. + base (float): the base of the log. Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be + used in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Classical relative entropy between :math:`\\mathbf{p}` and :math:`\\mathbf{q}`. + """ + backend = _check_backend(backend) + + if isinstance(prob_dist_p, list): + # np.float64 is necessary instead of native float because of tensorflow + prob_dist_p = backend.cast(prob_dist_p, dtype=np.float64) + if isinstance(prob_dist_q, list): + # np.float64 is necessary instead of native float because of tensorflow + prob_dist_q = backend.cast(prob_dist_q, dtype=np.float64) + + if (len(prob_dist_p.shape) != 1) or (len(prob_dist_q.shape) != 1): + raise_error( + TypeError, + "Probability arrays must have dims (k,) but have " + + f"dims {prob_dist_p.shape} and {prob_dist_q.shape}.", + ) + + if (len(prob_dist_p) == 0) or (len(prob_dist_q) == 0): + raise_error(TypeError, "At least one of the arrays is empty.") + + if base <= 0: + raise_error(ValueError, "log base must be non-negative.") + + if (any(prob_dist_p < 0) or any(prob_dist_p > 1.0)) or ( + any(prob_dist_q < 0) or any(prob_dist_q > 1.0) + ): + raise_error( + ValueError, + "All elements of the probability array must be between 0. and 1..", + ) + if np.abs(np.sum(prob_dist_p) - 1.0) > PRECISION_TOL: + raise_error(ValueError, "First probability array must sum to 1.") + + if np.abs(np.sum(prob_dist_q) - 1.0) > PRECISION_TOL: + raise_error(ValueError, "Second probability array must sum to 1.") + + entropy_p = -1 * shannon_entropy(prob_dist_p, base=base, backend=backend) + + log_prob_q = np.where( + prob_dist_q != 0.0, np.log2(prob_dist_q) / np.log2(base), -np.inf + ) + + log_prob = np.where(prob_dist_p != 0.0, log_prob_q, 0.0) + + relative = np.sum(prob_dist_p * log_prob) + + return entropy_p - relative + + +def classical_renyi_entropy( + prob_dist, alpha: Union[float, int], base: float = 2, backend=None +): + """Calculates the classical Rényi entropy :math:`H_{\\alpha}` of a discrete probability distribution. + + For :math:`\\alpha \\in (0, \\, 1) \\cup (1, \\, \\infty)` and probability distribution + :math:`\\mathbf{p}`, the classical Rényi entropy is defined as + + .. math:: + H_{\\alpha}(\\mathbf{p}) = \\frac{1}{1 - \\alpha} \\, \\log\\left( \\sum_{x} + \\, \\mathbf{p}^{\\alpha}(x) \\right) \\, . + + A special case is the limit :math:`\\alpha \\to 1`, in which the classical Rényi entropy + coincides with the :func:`qibo.quantum_info.entropies.shannon_entropy`. + + Another special case is the limit :math:`\\alpha \\to 0`, where the function is + reduced to :math:`\\log\\left(|\\mathbf{p}|\\right)`, with :math:`|\\mathbf{p}|` + being the support of :math:`\\mathbf{p}`. + This is known as the `Hartley entropy `_ + (also known as *Hartley function* or *max-entropy*). + + In the limit :math:`\\alpha \\to \\infty`, the function reduces to + :math:`-\\log(\\max_{x}(\\mathbf{p}(x)))`, which is called the + `min-entropy `_. + + Args: + prob_dist (ndarray): discrete probability distribution. + alpha (float or int): order of the Rényi entropy. + base (float): the base of the log. Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be + used in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Classical Rényi entropy :math:`H_{\\alpha}`. + """ + backend = _check_backend(backend) + + if isinstance(prob_dist, list): + # np.float64 is necessary instead of native float because of tensorflow + prob_dist = backend.cast(prob_dist, dtype=np.float64) + + if not isinstance(alpha, (float, int)): + raise_error( + TypeError, f"alpha must be type float, but it is type {type(alpha)}." + ) + + if alpha < 0.0: + raise_error(ValueError, "alpha must a non-negative float.") + + if base <= 0: + raise_error(ValueError, "log base must be non-negative.") + + if len(prob_dist.shape) != 1: + raise_error( + TypeError, + f"Probability array must have dims (k,) but it has {prob_dist.shape}.", + ) + + if len(prob_dist) == 0: + raise_error(TypeError, "Empty array.") + + if any(prob_dist < 0) or any(prob_dist > 1.0): + raise_error( + ValueError, + "All elements of the probability array must be between 0. and 1..", + ) + + if np.abs(np.sum(prob_dist) - 1.0) > PRECISION_TOL: + raise_error(ValueError, "Probability array must sum to 1.") + + if alpha == 0.0: + return np.log2(len(prob_dist)) / np.log2(base) + + if alpha == 1.0: + return shannon_entropy(prob_dist, base=base, backend=backend) + + if alpha == np.inf: + return -1 * np.log2(max(prob_dist)) / np.log2(base) + + renyi_ent = (1 / (1 - alpha)) * np.log2(np.sum(prob_dist**alpha)) / np.log2(base) + + return renyi_ent + + +def classical_relative_renyi_entropy( + prob_dist_p, prob_dist_q, alpha: Union[float, int], base: float = 2, backend=None +): + """Calculates the classical relative Rényi entropy between two discrete probability distributions. + + This function is also known as + `Rényi divergence `_. + + For :math:`\\alpha \\in (0, \\, 1) \\cup (1, \\, \\infty)` and probability distributions + :math:`\\mathbf{p}` and :math:`\\mathbf{q}`, the classical relative Rényi entropy is defined as + + .. math:: + H_{\\alpha}(\\mathbf{p} \\, \\| \\, \\mathbf{q}) = \\frac{1}{\\alpha - 1} \\, + \\log\\left( \\sum_{x} \\, \\frac{\\mathbf{p}^{\\alpha}(x)} + {\\mathbf{q}^{\\alpha - 1}(x)} \\right) \\, . + + A special case is the limit :math:`\\alpha \\to 1`, in which the classical Rényi divergence + coincides with the :func:`qibo.quantum_info.entropies.classical_relative_entropy`. + + Another special case is the limit :math:`\\alpha \\to 1/2`, where the function is + reduced to :math:`-2 \\log\\left(\\sum_{x} \\, \\sqrt{\\mathbf{p}(x) \\, \\mathbf{q}(x)} \\right)`. + The sum inside the :math:`\\log` is known as the + `Bhattacharyya coefficient `_. + + In the limit :math:`\\alpha \\to \\infty`, the function reduces to + :math:`\\log(\\max_{x}(\\mathbf{p}(x) \\, \\mathbf{q}(x))`. + + Args: + prob_dist_p (ndarray or list): discrete probability distribution :math:`p`. + prob_dist_q (ndarray or list): discrete probability distribution :math:`q`. + alpha (float or int): order of the Rényi entropy. + base (float): the base of the log. Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be + used in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Classical relative Rényi entropy :math:`H_{\\alpha}(\\mathbf{p} \\, \\| \\, \\mathbf{q})`. + """ + backend = _check_backend(backend) + + if isinstance(prob_dist_p, list): + # np.float64 is necessary instead of native float because of tensorflow + prob_dist_p = backend.cast(prob_dist_p, dtype=np.float64) + if isinstance(prob_dist_q, list): + # np.float64 is necessary instead of native float because of tensorflow + prob_dist_q = backend.cast(prob_dist_q, dtype=np.float64) + + if (len(prob_dist_p.shape) != 1) or (len(prob_dist_q.shape) != 1): + raise_error( + TypeError, + "Probability arrays must have dims (k,) but have " + + f"dims {prob_dist_p.shape} and {prob_dist_q.shape}.", + ) + + if (len(prob_dist_p) == 0) or (len(prob_dist_q) == 0): + raise_error(TypeError, "At least one of the arrays is empty.") + + if not isinstance(alpha, (float, int)): + raise_error( + TypeError, f"alpha must be type float, but it is type {type(alpha)}." + ) + + if alpha < 0.0: + raise_error(ValueError, "alpha must a non-negative float.") + + if base <= 0: + raise_error(ValueError, "log base must be non-negative.") + + if (any(prob_dist_p < 0) or any(prob_dist_p > 1.0)) or ( + any(prob_dist_q < 0) or any(prob_dist_q > 1.0) + ): + raise_error( + ValueError, + "All elements of the probability array must be between 0. and 1..", + ) + if np.abs(np.sum(prob_dist_p) - 1.0) > PRECISION_TOL: + raise_error(ValueError, "First probability array must sum to 1.") + + if np.abs(np.sum(prob_dist_q) - 1.0) > PRECISION_TOL: + raise_error(ValueError, "Second probability array must sum to 1.") + + if alpha == 0.5: + return -2 * np.log2(np.sum(np.sqrt(prob_dist_p * prob_dist_q))) / np.log2(base) + + if alpha == 1.0: + return classical_relative_entropy( + prob_dist_p, prob_dist_q, base=base, backend=backend + ) + + if alpha == np.inf: + return np.log2(max(prob_dist_p / prob_dist_q)) / np.log2(base) + + prob_p = prob_dist_p**alpha + prob_q = prob_dist_q ** (1 - alpha) + + return (1 / (alpha - 1)) * np.log2(np.sum(prob_p * prob_q)) / np.log2(base) + + +def classical_tsallis_entropy(prob_dist, alpha: float, base: float = 2, backend=None): + """Calculates the classical Tsallis entropy for a discrete probability distribution. + + This is defined as + + .. math:: + S_{\\alpha}(\\mathbf{p}) = \\frac{1}{\\alpha - 1} \\, + \\left(1 - \\sum_{x} \\, \\mathbf{p}^{\\alpha}(x) \\right) + + Args: + prob_dist (ndarray): discrete probability distribution. + alpha (float or int): entropic index. + base (float): the base of the log. Used when ``alpha=1.0``. + Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be + used in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Classical Tsallis entropy :math:`S_{\\alpha}(\\mathbf{p})`. + """ + backend = _check_backend(backend) + + if isinstance(prob_dist, list): + # np.float64 is necessary instead of native float because of tensorflow + prob_dist = backend.cast(prob_dist, dtype=np.float64) + + if not isinstance(alpha, (float, int)): + raise_error( + TypeError, f"alpha must be type float, but it is type {type(alpha)}." + ) + + if alpha < 0.0: + raise_error(ValueError, "alpha must a non-negative float.") + + if base <= 0: + raise_error(ValueError, "log base must be non-negative.") + + if len(prob_dist.shape) != 1: + raise_error( + TypeError, + f"Probability array must have dims (k,) but it has {prob_dist.shape}.", + ) + + if len(prob_dist) == 0: + raise_error(TypeError, "Empty array.") + + if any(prob_dist < 0) or any(prob_dist > 1.0): + raise_error( + ValueError, + "All elements of the probability array must be between 0. and 1..", + ) + + if np.abs(np.sum(prob_dist) - 1.0) > PRECISION_TOL: + raise_error(ValueError, "Probability array must sum to 1.") + + if alpha == 1.0: + return shannon_entropy(prob_dist, base=base, backend=backend) + + return (1 / (1 - alpha)) * (np.sum(prob_dist**alpha) - 1) + + +def von_neumann_entropy( + state, + base: float = 2, + check_hermitian: bool = False, + return_spectrum: bool = False, + backend=None, +): + """Calculates the von-Neumann entropy :math:`S(\\rho)` of a quantum ``state`` :math:`\\rho`. + + It is given by + + .. math:: + S(\\rho) = - \\text{tr}\\left[\\rho \\, \\log(\\rho)\\right] + + Args: + state (ndarray): statevector or density matrix. + base (float, optional): the base of the log. Defaults to :math:`2`. + check_hermitian (bool, optional): if ``True``, checks if ``state`` is Hermitian. + If ``False``, it assumes ``state`` is Hermitian . + Defaults to ``False``. + return_spectrum: if ``True``, returns ``entropy`` and + :math:`-\\log_{\\textup{b}}(\\textup{eigenvalues})`, where :math:`b` is ``base``. + If ``False``, returns only ``entropy``. Default is ``False``. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: The von-Neumann entropy :math:`S` of ``state`` :math:`\\rho`. + """ + backend = _check_backend(backend) + + if ( + (len(state.shape) >= 3) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + if base <= 0.0: + raise_error(ValueError, "log base must be non-negative.") + + if not isinstance(check_hermitian, bool): + raise_error( + TypeError, + f"check_hermitian must be type bool, but it is type {type(check_hermitian)}.", + ) + + if purity(state) == 1.0: + if return_spectrum: + return 0.0, backend.cast([1.0], dtype=float) + + return 0.0 + + if not check_hermitian or _check_hermitian_or_not_gpu(state, backend=backend): + eigenvalues = np.linalg.eigvalsh(state) + else: + eigenvalues = np.linalg.eigvals(state) + + log_prob = np.where(eigenvalues > 0, np.log2(eigenvalues) / np.log2(base), 0.0) + + ent = -np.sum(eigenvalues * log_prob) + # absolute value if entropy == 0.0 to avoid returning -0.0 + ent = np.abs(ent) if ent == 0.0 else ent + + ent = float(ent) + + if return_spectrum: + log_prob = backend.cast(log_prob, dtype=log_prob.dtype) + return ent, -log_prob + + return ent + + +def relative_von_neumann_entropy( + state, target, base: float = 2, check_hermitian: bool = False, backend=None +): + """Calculates the relative entropy :math:`S(\\rho \\, \\| \\, \\sigma)` between ``state`` :math:`\\rho` and ``target`` :math:`\\sigma`. + + It is given by + + .. math:: + S(\\rho \\, \\| \\, \\sigma) = \\text{tr}\\left[\\rho \\, \\log(\\rho)\\right] + - \\text{tr}\\left[\\rho \\, \\log(\\sigma)\\right] + + Args: + state (ndarray): statevector or density matrix :math:`\\rho`. + target (ndarray): statevector or density matrix :math:`\\sigma`. + base (float, optional): the base of the log. Defaults to :math:`2`. + check_hermitian (bool, optional): If ``True``, checks if ``state`` is Hermitian. + If ``False``, it assumes ``state`` is Hermitian . + Defaults to ``False``. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Relative (von-Neumann) entropy :math:`S(\\rho \\, \\| \\, \\sigma)`. + """ + backend = _check_backend(backend) + + if ( + (len(state.shape) >= 3) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + if ( + (len(target.shape) >= 3) + or (len(target) == 0) + or (len(target.shape) == 2 and target.shape[0] != target.shape[1]) + ): + raise_error( + TypeError, + f"target must have dims either (k,) or (k,k), but have dims {target.shape}.", + ) + + if base <= 0.0: + raise_error(ValueError, "log base must be non-negative.") + + if not isinstance(check_hermitian, bool): + raise_error( + TypeError, + f"check_hermitian must be type bool, but it is type {type(check_hermitian)}.", + ) + + if purity(state) == 1.0 and purity(target) == 1.0: + return 0.0 + + if len(state.shape) == 1: + state = np.outer(state, np.conj(state)) + + if len(target.shape) == 1: + target = np.outer(target, np.conj(target)) + + if not check_hermitian or _check_hermitian_or_not_gpu(state, backend=backend): + eigenvalues_state = np.linalg.eigvalsh(state) + else: + eigenvalues_state = np.linalg.eigvals(state) + + if not check_hermitian or _check_hermitian_or_not_gpu(target, backend=backend): + eigenvalues_target = np.linalg.eigvalsh(target) + else: + eigenvalues_target = np.linalg.eigvals(target) + + log_state = np.where( + eigenvalues_state > 0, np.log2(eigenvalues_state) / np.log2(base), 0.0 + ) + log_target = np.where( + eigenvalues_target > 0, np.log2(eigenvalues_target) / np.log2(base), -np.inf + ) + + log_target = np.where(eigenvalues_state != 0.0, log_target, 0.0) + + entropy_state = np.sum(eigenvalues_state * log_state) + + relative = np.sum(eigenvalues_state * log_target) + + return float(entropy_state - relative) + + +def renyi_entropy(state, alpha: Union[float, int], base: float = 2, backend=None): + """Calculates the Rényi entropy :math:`H_{\\alpha}` of a quantum state :math:`\\rho`. + + For :math:`\\alpha \\in (0, \\, 1) \\cup (1, \\, \\infty)`, the Rényi entropy is defined as + + .. math:: + H_{\\alpha}(\\rho) = \\frac{1}{1 - \\alpha} \\, \\log\\left( \\rho^{\\alpha} \\right) \\, . + + A special case is the limit :math:`\\alpha \\to 1`, in which the Rényi entropy + coincides with the :func:`qibo.quantum_info.entropies.entropy`. + + Another special case is the limit :math:`\\alpha \\to 0`, where the function is + reduced to :math:`\\log\\left(d\\right)`, with :math:`d = 2^{n}` + being the dimension of the Hilbert space in which ``state`` :math:`\\rho` lives in. + This is known as the `Hartley entropy `_ + (also known as *Hartley function* or *max-entropy*). + + In the limit :math:`\\alpha \\to \\infty`, the function reduces to + :math:`-\\log(\\|\\rho\\|_{\\infty})`, with :math:`\\|\\cdot\\|_{\\infty}` + being the `spectral norm `_. + This is known as the `min-entropy `_. + + Args: + state (ndarray): statevector or density matrix. + alpha (float or int): order of the Rényi entropy. + base (float): the base of the log. Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be + used in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Rényi entropy :math:`H_{\\alpha}`. + """ + backend = _check_backend(backend) + + if ( + (len(state.shape) >= 3) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + if not isinstance(alpha, (float, int)): + raise_error( + TypeError, f"alpha must be type float, but it is type {type(alpha)}." + ) + + if alpha < 0.0: + raise_error(ValueError, "alpha must a non-negative float.") + + if base <= 0.0: + raise_error(ValueError, "log base must be non-negative.") + + if abs(purity(state) - 1.0) < PRECISION_TOL: + return 0.0 + + if alpha == 0.0: + return np.log2(len(state)) / np.log2(base) + + if alpha == 1.0: + return von_neumann_entropy(state, base=base, backend=backend) + + if alpha == np.inf: + return ( + -1 + * np.log2(backend.calculate_norm_density_matrix(state, order=2)) + / np.log2(base) + ) + + log = np.log2(np.trace(_matrix_power(state, alpha, backend))) + + return (1 / (1 - alpha)) * log / np.log2(base) + + +def relative_renyi_entropy( + state, target, alpha: Union[float, int], base: float = 2, backend=None +): + """Calculates the relative Rényi entropy between two quantum states. + + For :math:`\\alpha \\in (0, \\, 1) \\cup (1, \\, \\infty)` and quantum states + :math:`\\rho` and :math:`\\sigma`, the relative Rényi entropy is defined as + + .. math:: + H_{\\alpha}(\\rho \\, \\| \\, \\sigma) = \\frac{1}{\\alpha - 1} \\, + \\log\\left( \\textup{tr}\\left( \\rho^{\\alpha} \\, + \\sigma^{1 - \\alpha} \\right) \\right) \\, . + + A special case is the limit :math:`\\alpha \\to 1`, in which the Rényi entropy + coincides with the :func:`qibo.quantum_info.entropies.relative_entropy`. + + In the limit :math:`\\alpha \\to \\infty`, the function reduces to + :math:`-2 \\, \\log(\\|\\sqrt{\\rho} \\, \\sqrt{\\sigma}\\|_{1})`, + with :math:`\\|\\cdot\\|_{1}` being the + `Schatten 1-norm `_. + This is known as the `min-relative entropy `_. + + .. note:: + Function raises ``NotImplementedError`` when ``target`` :math:`sigma` + is a pure state and :math:`\\alpha > 1`. This is due to the fact that + it is not possible to calculate :math:`\\sigma^{1 - \\alpha}` when + :math:`\\alpha > 1` and :math:`\\sigma` is a projector, i.e. a singular matrix. + + Args: + state (ndarray): statevector or density matrix :math:`\\rho`. + target (ndarray): statevector or density matrix :math:`\\sigma`. + alpha (float or int): order of the Rényi entropy. + base (float): the base of the log. Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be + used in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Relative Rényi entropy :math:`H_{\\alpha}(\\rho \\, \\| \\, \\sigma)`. + """ + backend = _check_backend(backend) + + if ( + (len(state.shape) >= 3) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + if ( + (len(target.shape) >= 3) + or (len(target) == 0) + or (len(target.shape) == 2 and target.shape[0] != target.shape[1]) + ): + raise_error( + TypeError, + f"target must have dims either (k,) or (k,k), but have dims {target.shape}.", + ) + + if not isinstance(alpha, (float, int)): + raise_error( + TypeError, f"alpha must be type float, but it is type {type(alpha)}." + ) + + if alpha < 0.0: + raise_error(ValueError, "alpha must a non-negative float.") + + if base <= 0.0: + raise_error(ValueError, "log base must be non-negative.") + + purity_target = purity(target) + if ( + abs(purity(state) - 1.0) < PRECISION_TOL + and abs(purity_target - 1) < PRECISION_TOL + ): + return 0.0 + + if alpha > 1.0 and abs(purity_target - 1) < PRECISION_TOL: + raise_error( + NotImplementedError, + "It is not possible to invert a singular matrix. ``target`` is a pure state and alpha > 1.", + ) + + if len(state.shape) == 1: + state = np.outer(state, np.conj(state)) + + if alpha == 1.0: + return relative_von_neumann_entropy(state, target, base, backend=backend) + + if alpha == np.inf: + new_state = _matrix_power(state, 0.5, backend) + new_target = _matrix_power(target, 0.5, backend) + + log = np.log2( + backend.calculate_norm_density_matrix(new_state @ new_target, order=1) + ) + + return -2 * log / np.log2(base) + + log = _matrix_power(state, alpha, backend) + log = log @ _matrix_power(target, 1 - alpha, backend) + log = np.log2(np.trace(log)) + + return (1 / (alpha - 1)) * log / np.log2(base) + + +def tsallis_entropy(state, alpha: float, base: float = 2, backend=None): + """Calculates the Tsallis entropy of a quantum state. + + .. math:: + S_{\\alpha}(\\rho) = \\frac{1}{1 - \\alpha} \\, + \\left( \\text{tr}(\\rho^{\\alpha}) - 1 \\right) + + When :math:`\\alpha = 1`, the functions defaults to + :func:`qibo.quantum_info.entropies.entropy`. + + Args: + state (ndarray): statevector or density matrix. + alpha (float or int): entropic index. + base (float, optional): the base of the log. Used when ``alpha=1.0``. + Defaults to :math:`2`. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Tsallis entropy :math:`S_{\\alpha}(\\rho)`. + """ + backend = _check_backend(backend) + + if ( + (len(state.shape) >= 3) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + if not isinstance(alpha, (float, int)): + raise_error( + TypeError, f"alpha must be type float, but it is type {type(alpha)}." + ) + + if alpha < 0.0: + raise_error(ValueError, "alpha must a non-negative float.") + + if base <= 0.0: + raise_error(ValueError, "log base must be non-negative.") + + if abs(purity(state) - 1.0) < PRECISION_TOL: + return 0.0 + + if alpha == 1.0: + return von_neumann_entropy(state, base=base, backend=backend) + + return (1 / (1 - alpha)) * (np.trace(_matrix_power(state, alpha, backend)) - 1) + + +def entanglement_entropy( + state, + bipartition, + base: float = 2, + check_hermitian: bool = False, + return_spectrum: bool = False, + backend=None, +): + """Calculates the entanglement entropy :math:`S` of bipartition :math:`A` + of ``state`` :math:`\\rho`. This is given by + + .. math:: + S(\\rho_{A}) = -\\text{tr}(\\rho_{A} \\, \\log(\\rho_{A})) \\, , + + where :math:`\\rho_{A} = \\text{tr}_{B}(\\rho)` is the reduced density matrix calculated + by tracing out the ``bipartition`` :math:`B`. + + Args: + state (ndarray): statevector or density matrix. + bipartition (list or tuple or ndarray): qubits in the subsystem to be traced out. + base (float, optional): the base of the log. Defaults to :math: `2`. + check_hermitian (bool, optional): if ``True``, checks if :math:`\\rho_{A}` is Hermitian. + If ``False``, it assumes ``state`` is Hermitian . Default: ``False``. + return_spectrum: if ``True``, returns ``entropy`` and eigenvalues of ``state``. + If ``False``, returns only ``entropy``. Default is ``False``. + backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used + in the execution. If ``None``, it uses + :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. + + Returns: + float: Entanglement entropy :math:`S` of ``state`` :math:`\\rho`. + """ + backend = _check_backend(backend) + + if base <= 0.0: + raise_error(ValueError, "log base must be non-negative.") + + if ( + (len(state.shape) not in [1, 2]) + or (len(state) == 0) + or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) + ): + raise_error( + TypeError, + f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", + ) + + nqubits = int(np.log2(state.shape[0])) + + reduced_density_matrix = ( + backend.partial_trace(state, bipartition, nqubits) + if len(state.shape) == 1 + else backend.partial_trace_density_matrix(state, bipartition, nqubits) + ) + + entropy_entanglement = von_neumann_entropy( + reduced_density_matrix, + base=base, + check_hermitian=check_hermitian, + return_spectrum=return_spectrum, + backend=backend, + ) + + return entropy_entanglement + + +def _matrix_power(matrix, alpha, backend): + """Calculates ``matrix ** alpha`` according to backend.""" + if backend.__class__.__name__ in [ + "CupyBackend", + "CuQuantumBackend", + ]: # pragma: no cover + new_matrix = backend.to_numpy(matrix) + else: + new_matrix = np.copy(matrix) + + if len(new_matrix.shape) == 1: + new_matrix = np.outer(new_matrix, np.conj(new_matrix)) + + new_matrix = fractional_matrix_power(new_matrix, alpha) + + return backend.cast(new_matrix, dtype=new_matrix.dtype) diff --git a/src/qibo/quantum_info/metrics.py b/src/qibo/quantum_info/metrics.py index eb29f1b486..08dbf777af 100644 --- a/src/qibo/quantum_info/metrics.py +++ b/src/qibo/quantum_info/metrics.py @@ -5,7 +5,7 @@ import numpy as np from scipy import sparse -from qibo.backends import GlobalBackend +from qibo.backends import _check_backend from qibo.config import PRECISION_TOL, raise_error @@ -57,273 +57,6 @@ def impurity(state): return 1 - purity(state) -def concurrence(state, bipartition, check_purity: bool = True, backend=None): - """Calculates concurrence of a pure bipartite quantum state - :math:`\\rho \\in \\mathcal{H}_{A} \\otimes \\mathcal{H}_{B}` as - - .. math:: - C(\\rho) = \\sqrt{2 \\, (\\text{tr}^{2}(\\rho) - \\text{tr}(\\rho_{A}^{2}))} \\, , - - where :math:`\\rho_{A} = \\text{tr}_{B}(\\rho)` is the reduced density operator - obtained by tracing out the qubits in the ``bipartition`` :math:`B`. - - Args: - state (ndarray): statevector or density matrix. - bipartition (list or tuple or ndarray): qubits in the subsystem to be traced out. - check_purity (bool, optional): if ``True``, checks if ``state`` is pure. If ``False``, - it assumes ``state`` is pure . Defaults to ``True``. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. - Defaults to ``None``. - - Returns: - float: Concurrence of :math:`\\rho`. - """ - if backend is None: # pragma: no cover - backend = GlobalBackend() - - if ( - (len(state.shape) not in [1, 2]) - or (len(state) == 0) - or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) - ): - raise_error( - TypeError, - f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", - ) - - if isinstance(check_purity, bool) is False: - raise_error( - TypeError, - f"check_purity must be type bool, but it is type {type(check_purity)}.", - ) - - nqubits = int(np.log2(state.shape[0])) - - if check_purity is True: - purity_total_system = purity(state) - - mixed = bool(abs(purity_total_system - 1.0) > PRECISION_TOL) - if mixed is True: - raise_error( - NotImplementedError, - "concurrence only implemented for pure quantum states.", - ) - - reduced_density_matrix = ( - backend.partial_trace(state, bipartition, nqubits) - if len(state.shape) == 1 - else backend.partial_trace_density_matrix(state, bipartition, nqubits) - ) - - purity_reduced = purity(reduced_density_matrix) - if purity_reduced - 1.0 > 0.0: - purity_reduced = round(purity_reduced, 7) - - concur = np.sqrt(2 * (1 - purity_reduced)) - - return concur - - -def entanglement_of_formation( - state, bipartition, base: float = 2, check_purity: bool = True, backend=None -): - """Calculates the entanglement of formation :math:`E_{f}` of a pure bipartite - quantum state :math:`\\rho`, which is given by - - .. math:: - E_{f} = H([1 - x, x]) \\, , - - where - - .. math:: - x = \\frac{1 + \\sqrt{1 - C^{2}(\\rho)}}{2} \\, , - - :math:`C(\\rho)` is the :func:`qibo.quantum_info.concurrence` of :math:`\\rho`, - and :math:`H` is the :func:`qibo.quantum_info.shannon_entropy`. - - Args: - state (ndarray): statevector or density matrix. - bipartition (list or tuple or ndarray): qubits in the subsystem to be traced out. - base (float): the base of the log in :func:`qibo.quantum_info.shannon_entropy`. - Defaults to :math:`2`. - check_purity (bool, optional): if ``True``, checks if ``state`` is pure. If ``False``, - it assumes ``state`` is pure . Default: ``True``. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. - Defaults to ``None``. - - - Returns: - float: entanglement of formation of state :math:`\\rho`. - """ - if backend is None: # pragma: no cover - backend = GlobalBackend() - - from qibo.quantum_info.utils import shannon_entropy # pylint: disable=C0415 - - concur = concurrence( - state, bipartition=bipartition, check_purity=check_purity, backend=backend - ) - concur = (1 + np.sqrt(1 - concur**2)) / 2 - probabilities = [1 - concur, concur] - - ent_of_form = shannon_entropy(probabilities, base=base, backend=backend) - - return ent_of_form - - -def entropy( - state, - base: float = 2, - check_hermitian: bool = False, - return_spectrum: bool = False, - backend=None, -): - """The von-Neumann entropy :math:`S(\\rho)` of a quantum ``state`` :math:`\\rho`, which - is given by - - .. math:: - S(\\rho) = - \\text{tr}\\left[\\rho \\, \\log(\\rho)\\right] - - Args: - state (ndarray): statevector or density matrix. - base (float, optional): the base of the log. Defaults to :math:`2`. - check_hermitian (bool, optional): if ``True``, checks if ``state`` is Hermitian. - If ``False``, it assumes ``state`` is Hermitian . - Defaults to ``False``. - return_spectrum: if ``True``, returns ``entropy`` and - :math:`-\\log_{\\textup{b}}(\\textup{eigenvalues})`, where :math:`b` is ``base``. - If ``False``, returns only ``entropy``. Default is ``False``. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses - :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. - - Returns: - float: The von-Neumann entropy :math:`S` of ``state`` :math:`\\rho`. - """ - if backend is None: # pragma: no cover - backend = GlobalBackend() - - if ( - (len(state.shape) >= 3) - or (len(state) == 0) - or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) - ): - raise_error( - TypeError, - f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", - ) - - if base <= 0.0: - raise_error(ValueError, "log base must be non-negative.") - - if isinstance(check_hermitian, bool) is False: - raise_error( - TypeError, - f"check_hermitian must be type bool, but it is type {type(check_hermitian)}.", - ) - - if purity(state) == 1.0: - if return_spectrum: - return 0.0, backend.cast([1.0], dtype=float) - - return 0.0 - - if check_hermitian is False or _check_hermitian_or_not_gpu(state, backend=backend): - eigenvalues = np.linalg.eigvalsh(state) - else: - eigenvalues = np.linalg.eigvals(state) - - if base == 2: - log_prob = np.where(eigenvalues > 0, np.log2(eigenvalues), 0.0) - elif base == 10: - log_prob = np.where(eigenvalues > 0, np.log10(eigenvalues), 0.0) - elif base == np.e: - log_prob = np.where(eigenvalues > 0, np.log(eigenvalues), 0.0) - else: - log_prob = np.where(eigenvalues > 0, np.log(eigenvalues) / np.log(base), 0.0) - - ent = -np.sum(eigenvalues * log_prob) - # absolute value if entropy == 0.0 to avoid returning -0.0 - ent = np.abs(ent) if ent == 0.0 else ent - - ent = float(ent) - - if return_spectrum: - log_prob = backend.cast(log_prob, dtype=log_prob.dtype) - return ent, -log_prob - - return ent - - -def entanglement_entropy( - state, - bipartition, - base: float = 2, - check_hermitian: bool = False, - return_spectrum: bool = False, - backend=None, -): - """Calculates the entanglement entropy :math:`S` of bipartition :math:`A` - of ``state`` :math:`\\rho`. This is given by - - .. math:: - S(\\rho_{A}) = -\\text{tr}(\\rho_{A} \\, \\log(\\rho_{A})) \\, , - - where :math:`\\rho_{A} = \\text{tr}_{B}(\\rho)` is the reduced density matrix calculated - by tracing out the ``bipartition`` :math:`B`. - - Args: - state (ndarray): statevector or density matrix. - bipartition (list or tuple or ndarray): qubits in the subsystem to be traced out. - base (float, optional): the base of the log. Defaults to :math: `2`. - check_hermitian (bool, optional): if ``True``, checks if :math:`\\rho_{A}` is Hermitian. - If ``False``, it assumes ``state`` is Hermitian . Default: ``False``. - return_spectrum: if ``True``, returns ``entropy`` and eigenvalues of ``state``. - If ``False``, returns only ``entropy``. Default is ``False``. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses - :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. - - Returns: - float: Entanglement entropy :math:`S` of ``state`` :math:`\\rho`. - """ - if backend is None: # pragma: no cover - backend = GlobalBackend() - - if base <= 0.0: - raise_error(ValueError, "log base must be non-negative.") - - if ( - (len(state.shape) not in [1, 2]) - or (len(state) == 0) - or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) - ): - raise_error( - TypeError, - f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", - ) - - nqubits = int(np.log2(state.shape[0])) - - reduced_density_matrix = ( - backend.partial_trace(state, bipartition, nqubits) - if len(state.shape) == 1 - else backend.partial_trace_density_matrix(state, bipartition, nqubits) - ) - - entropy_entanglement = entropy( - reduced_density_matrix, - base=base, - check_hermitian=check_hermitian, - return_spectrum=return_spectrum, - backend=backend, - ) - - return entropy_entanglement - - def trace_distance(state, target, check_hermitian: bool = False, backend=None): """Trace distance between two quantum states, :math:`\\rho` and :math:`\\sigma`: @@ -348,8 +81,7 @@ def trace_distance(state, target, check_hermitian: bool = False, backend=None): Returns: float: Trace distance between ``state`` :math:`\\rho` and ``target`` :math:`\\sigma`. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if state.shape != target.shape: raise_error( @@ -470,8 +202,7 @@ def fidelity(state, target, check_hermitian: bool = False, backend=None): Returns: float: Fidelity between ``state`` :math:`\\rho` and ``target`` :math:`\\sigma`. """ - if backend is None: - backend = GlobalBackend() + backend = _check_backend(backend) if state.shape != target.shape: raise_error( @@ -634,84 +365,6 @@ def bures_distance(state, target, check_hermitian: bool = False, backend=None): return distance -def entanglement_fidelity( - channel, nqubits: int, state=None, check_hermitian: bool = False, backend=None -): - """Entanglement fidelity :math:`F_{\\mathcal{E}}` of a ``channel`` :math:`\\mathcal{E}` - on ``state`` :math:`\\rho` is given by - - .. math:: - F_{\\mathcal{E}}(\\rho) = F(\\rho_{f}, \\rho) - - where :math:`F` is the :func:`qibo.quantum_info.fidelity` function for states, - and :math:`\\rho_{f} = \\mathcal{E}_{A} \\otimes I_{B}(\\rho)` - is the state after the channel :math:`\\mathcal{E}` was applied to - partition :math:`A`. - - Args: - channel (:class:`qibo.gates.channels.Channel`): quantum channel - acting on partition :math:`A`. - nqubits (int): total number of qubits in ``state``. - state (ndarray, optional): statevector or density matrix to be evolved - by ``channel``. If ``None``, defaults to the maximally entangled state - :math:`\\frac{1}{2^{n}} \\, \\sum_{k} \\, \\ket{k}\\ket{k}`, where - :math:`n` is ``nqubits``. Defaults to ``None``. - check_hermitian (bool, optional): if ``True``, checks if the final state - :math:`\\rho_{f}` is Hermitian. If ``False``, it assumes it is Hermitian. - Defaults to ``False``. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. - Defaults to ``None``. - - Returns: - float: Entanglement fidelity :math:`F_{\\mathcal{E}}`. - """ - if backend is None: # pragma: no cover - backend = GlobalBackend() - - if isinstance(nqubits, int) is False: - raise_error( - TypeError, f"nqubits must be type int, but it is type {type(nqubits)}." - ) - - if nqubits <= 0: - raise_error( - ValueError, f"nqubits must be a positive integer, but it is {nqubits}." - ) - - if state is not None and ( - (len(state.shape) not in [1, 2]) - or (len(state) == 0) - or (len(state.shape) == 2 and state.shape[0] != state.shape[1]) - ): - raise_error( - TypeError, - f"state must have dims either (k,) or (k,k), but have dims {state.shape}.", - ) - - if isinstance(check_hermitian, bool) is False: - raise_error( - TypeError, - f"check_hermitian must be type bool, but it is type {type(check_hermitian)}.", - ) - - if state is None: - state = backend.plus_density_matrix(nqubits) - - # necessary because this function do support repeated execution, - # so it has to default to density matrices - if len(state.shape) == 1: - state = np.outer(state, np.conj(state)) - - state_final = backend.apply_channel_density_matrix(channel, state, nqubits) - - entang_fidelity = fidelity( - state_final, state, check_hermitian=check_hermitian, backend=backend - ) - - return entang_fidelity - - def process_fidelity(channel, target=None, check_unitary: bool = False, backend=None): """Process fidelity between a quantum ``channel`` :math:`\\mathcal{E}` and a ``target`` unitary channel :math:`U`. The process fidelity is defined as @@ -733,8 +386,7 @@ def process_fidelity(channel, target=None, check_unitary: bool = False, backend= Returns: float: Process fidelity between ``channel`` and ``target``. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if target is not None: if channel.shape != target.shape: @@ -938,8 +590,8 @@ def diamond_norm(channel, target=None, backend=None, **kwargs): # `CVXPY` only works with `numpy`, so this function has to # convert any channel to the `numpy` backend by default - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) + channel = backend.to_numpy(channel) channel = np.transpose(channel) @@ -1008,103 +660,6 @@ def diamond_norm(channel, target=None, backend=None, **kwargs): return solution -def meyer_wallach_entanglement(circuit, backend=None): - """Computes the Meyer-Wallach entanglement Q of the `circuit`, - - .. math:: - Q(\\theta) = 1 - \\frac{1}{N} \\, \\sum_{k} \\, - \\text{tr}\\left(\\rho_{k^{2}}(\\theta)\\right) \\, . - - Args: - circuit (:class:`qibo.models.Circuit`): Parametrized circuit. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. - Defaults to ``None``. - - Returns: - float: Meyer-Wallach entanglement. - """ - - if backend is None: # pragma: no cover - backend = GlobalBackend() - - circuit.density_matrix = True - nqubits = circuit.nqubits - - rho = backend.execute_circuit(circuit).state() - - ent = 0 - for j in range(nqubits): - trace_q = list(range(nqubits)) - trace_q.pop(j) - - rho_r = backend.partial_trace_density_matrix(rho, trace_q, nqubits) - - trace = purity(rho_r) - - ent += trace - - entanglement = 1 - ent / nqubits - - return entanglement - - -def entangling_capability(circuit, samples: int, seed=None, backend=None): - """Returns the entangling capability :math:`\\text{Ent}` of a parametrized - circuit, which is average Meyer-Wallach entanglement Q of the circuit, i.e. - - .. math:: - \\text{Ent} = \\frac{2}{S}\\sum_{k}Q_k \\, , - - where :math:`S` is the number of samples. - - Args: - circuit (:class:`qibo.models.Circuit`): Parametrized circuit. - samples (int): number of samples to estimate the integral. - seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random - numbers or a fixed seed to initialize a generator. If ``None``, initializes - a generator with a random seed. Default: ``None``. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. - Defaults to ``None``. - - Returns: - float: Entangling capability. - """ - - if isinstance(samples, int) is False: - raise_error( - TypeError, f"samples must be type int, but it is type {type(samples)}." - ) - - if ( - seed is not None - and not isinstance(seed, int) - and not isinstance(seed, np.random.Generator) - ): - raise_error( - TypeError, "seed must be either type int or numpy.random.Generator." - ) - - if backend is None: # pragma: no cover - backend = GlobalBackend() - - local_state = ( - np.random.default_rng(seed) if seed is None or isinstance(seed, int) else seed - ) - - res = [] - for _ in range(samples): - params = local_state.uniform(-np.pi, np.pi, circuit.trainable_gates.nparams) - circuit.set_parameters(params) - entanglement = meyer_wallach_entanglement(circuit, backend=backend) - res.append(entanglement) - - capability = 2 * np.real(np.sum(res)) / samples - - return capability - - def expressibility( circuit, power_t: int, @@ -1151,8 +706,7 @@ def expressibility( pqc_integral, ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) deviation = haar_integral( circuit.nqubits, power_t, samples=None, backend=backend @@ -1214,8 +768,7 @@ def frame_potential( TypeError, f"samples must be type int, but it is type {type(samples)}." ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) nqubits = circuit.nqubits dim = 2**nqubits @@ -1259,8 +812,7 @@ def _check_hermitian_or_not_gpu(matrix, backend=None): `backend` is not :class:`qibojit.backends.CupyBackend` """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) norm = backend.calculate_norm_density_matrix( np.transpose(np.conj(matrix)) - matrix, order=2 diff --git a/src/qibo/quantum_info/quantum_networks.py b/src/qibo/quantum_info/quantum_networks.py index af549a46fa..8992169ae5 100644 --- a/src/qibo/quantum_info/quantum_networks.py +++ b/src/qibo/quantum_info/quantum_networks.py @@ -1,4 +1,4 @@ -"""Module defining the QuantumNetwork class and adjacent functions.""" +"""Module defining the `QuantumNetwork` class and adjacent functions.""" import re from functools import reduce @@ -7,12 +7,26 @@ import numpy as np -from qibo.backends import GlobalBackend +from qibo.backends import _check_backend from qibo.config import raise_error class QuantumNetwork: - """Quantum network object that holds a Choi operator as a tensor. + """This class stores the Choi operator of the quantum network as a tensor, + which is an unique representation of the quantum network. + + A minimum quantum network is a quantum channel, which is a quantum network of the form + :math:`J[n \\to m]`, where :math:`n` is the dimension of the input system , + and :math:`m` is the dimension of the output system. + A quantum state is a quantum network of the form :math:`J[1 \\to n]`, + such that the input system is trivial. + An observable is a quantum network of the form :math:`J[n \\to 1]`, + such that the output system is trivial. + + A quantum network may contain multiple input and output systems. + For example, a "quantum comb" is a quantum network of the form :math:`J[n', n \\to m, m']`, + which convert a quantum channel of the form :math:`J[n \\to m]` + to a quantum channel of the form :math:`J[n' \\to m']`. Args: matrix (ndarray): input Choi operator. @@ -21,8 +35,8 @@ class QuantumNetwork: Choi operator. If ``None``, defaults to ``(False,True,False,True,...)``, where ``len(system_output)=len(partition)``. Defaults to ``None``. - pure (bool, optional): ``True`` when ``matrix`` is a rank-:math:`1` operator, - ``False`` otherwise. Defaults to ``False``. + pure (bool, optional): ``True`` when ``matrix`` is a "pure" representation (e.g. a pure + state, a unitary operator, etc.), ``False`` otherwise. Defaults to ``False``. backend (:class:`qibo.backends.abstract.Backend`, optional): Backend to be used in calculations. If ``None``, defaults to :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. @@ -64,11 +78,11 @@ def matrix(self, backend=None): return backend.cast(self._matrix, dtype=self._matrix.dtype) - def pure(self): + def is_pure(self): """Returns bool indicading if the Choi operator of the network is pure.""" return self._pure - def hermitian( + def is_hermitian( self, order: Optional[Union[int, str]] = None, precision_tol: float = 1e-8 ): """Returns bool indicating if the Choi operator :math:`\\mathcal{E}` of the network is Hermitian. @@ -114,7 +128,7 @@ def hermitian( return float(norm) <= precision_tol - def unital( + def is_unital( self, order: Optional[Union[int, str]] = None, precision_tol: float = 1e-8 ): """Returns bool indicating if the Choi operator :math:`\\mathcal{E}` of the network is unital. @@ -162,7 +176,7 @@ def unital( return float(norm) <= precision_tol - def causal( + def is_causal( self, order: Optional[Union[int, str]] = None, precision_tol: float = 1e-8 ): """Returns bool indicating if the Choi operator :math:`\\mathcal{E}` of the network satisfies the causal order condition. @@ -210,8 +224,8 @@ def causal( return float(norm) <= precision_tol - def positive_semidefinite(self, precision_tol: float = 1e-8): - """Returns bool indicating if Choi operator :math:`\\mathcal{E}` of the networn is positive-semidefinite. + def is_positive_semidefinite(self, precision_tol: float = 1e-8): + """Returns bool indicating if Choi operator :math:`\\mathcal{E}` of the network is positive-semidefinite. Args: precision_tol (float, optional): threshold value used to check if eigenvalues of @@ -228,7 +242,7 @@ def positive_semidefinite(self, precision_tol: float = 1e-8): reshaped = np.reshape(self._matrix, (self.dims, self.dims)) - if self.hermitian(): + if self.is_hermitian(): eigenvalues = np.linalg.eigvalsh(reshaped) else: if self._backend.__class__.__name__ in [ @@ -240,7 +254,7 @@ def positive_semidefinite(self, precision_tol: float = 1e-8): return all(eigenvalue >= -precision_tol for eigenvalue in eigenvalues) - def channel( + def is_channel( self, order: Optional[Union[int, str]] = None, precision_tol_causal: float = 1e-8, @@ -263,9 +277,9 @@ def channel( Returns: bool: Channel condition. """ - return self.causal(order, precision_tol_causal) and self.positive_semidefinite( - precision_tol_psd - ) + return self.is_causal( + order, precision_tol_causal + ) and self.is_positive_semidefinite(precision_tol_psd) def apply(self, state): """Apply the Choi operator :math:`\\mathcal{E}` to ``state`` :math:`\\varrho`. @@ -280,7 +294,7 @@ def apply(self, state): """ matrix = np.copy(self._matrix) - if self.pure(): + if self.is_pure(): return np.einsum("kj,ml,jl -> km", matrix, np.conj(matrix), state) return np.einsum("jklm,km -> jl", matrix, state) @@ -323,8 +337,8 @@ def link_product(self, second_network, subscripts: str = "ij,jk -> ik"): inv_subscripts = pattern_two and subscripts[0] == subscripts[4] super_subscripts = ( pattern_four - and subscripts[2] == subscripts[5] - and subscripts[3] == subscripts[6] + and subscripts[1] == subscripts[5] + and subscripts[2] == subscripts[6] ) if not channel_subscripts and not inv_subscripts and not super_subscripts: @@ -337,10 +351,10 @@ def link_product(self, second_network, subscripts: str = "ij,jk -> ik"): second_matrix = second_network._full() # pylint: disable=W0212 if super_subscripts: - cexpr = "jklmnopq,nopqrstu->jklmrstu" + cexpr = "jklmnopq,klop->jmnq" return QuantumNetwork( np.einsum(cexpr, first_matrix, second_matrix), - self.partition[:2] + second_network.partition[2:], + [self.partition[0] + self.partition[-1]], ) cexpr = "jkab,klbc->jlac" @@ -376,7 +390,7 @@ def to_full(self, backend=None): if backend is None: # pragma: no cover backend = self._backend - if self.pure(): + if self.is_pure(): self._matrix = self._full() self._pure = False @@ -426,6 +440,11 @@ def __add__(self, second_network): def __mul__(self, number: Union[float, int]): """Returns quantum network with its Choi operator multiplied by a scalar. + If the quantum network is pure and ``number > 0.0``, the method returns a pure quantum + network with its Choi operator multiplied by the square root of ``number``. + This is equivalent to multiplying `self.to_full()` by the ``number``. + Otherwise, this method will return a full quantum network. + Args: number (float or int): scalar to multiply the Choi operator of the network with. @@ -439,7 +458,7 @@ def __mul__(self, number: Union[float, int]): "It is not possible to multiply a ``QuantumNetwork`` by a non-scalar.", ) - if self.pure() and number > 0.0: + if self.is_pure() and number > 0.0: return QuantumNetwork( np.sqrt(number) * self.matrix(backend=self._backend), partition=self.partition, @@ -465,6 +484,11 @@ def __rmul__(self, number: Union[float, int]): def __truediv__(self, number: Union[float, int]): """Returns quantum network with its Choi operator divided by a scalar. + If the quantum network is pure and ``number > 0.0``, the method returns a pure quantum + network with its Choi operator divided by the square root of ``number``. + This is equivalent to dividing `self.to_full()` by the ``number``. + Otherwise, this method will return a full quantum network. + Args: number (float or int): scalar to divide the Choi operator of the network with. @@ -478,20 +502,13 @@ def __truediv__(self, number: Union[float, int]): "It is not possible to divide a ``QuantumNetwork`` by a non-scalar.", ) - if self.pure() and number > 0.0: - return QuantumNetwork( - self.matrix(backend=self._backend) / np.sqrt(number), - partition=self.partition, - system_output=self.system_output, - pure=True, - backend=self._backend, - ) + number = np.sqrt(number) if self.is_pure() and number > 0.0 else number return QuantumNetwork( self.matrix(backend=self._backend) / number, partition=self.partition, system_output=self.system_output, - pure=False, + pure=self.is_pure(), backend=self._backend, ) @@ -515,17 +532,38 @@ def __matmul__(self, second_network): + "``QuantumNetwork`` by a non-``QuantumNetwork``.", ) - if self.partition != second_network.partition: - raise_error( - ValueError, - "partitions of the networks do not match: " - + f"{self.partition} != {second_network.partition}.", - ) + if len(self.partition) == 2: # `self` is a channel + if len(second_network.partition) != 2: + raise_error( + ValueError, + f"`QuantumNetwork {second_network} is assumed to be a channel, but it is not. " + + "Use `link_product` method to specify the subscript.", + ) + if self.partition[1] != second_network.partition[0]: + raise_error( + ValueError, + "partitions of the networks do not match: " + + f"{self.partition[1]} != {second_network.partition[0]}.", + ) - if len(self.partition) == 2: subscripts = "jk,kl -> jl" - elif len(self.partition) == 4: - subscripts = "jklm,lmno -> jkno" + + elif len(self.partition) == 4: # `self` is a super-channel + if len(second_network.partition) != 2: + raise_error( + ValueError, + f"`QuantumNetwork {second_network} is assumed to be a channel, but it is not. " + + "Use `link_product` method to specify the subscript.", + ) + if self.partition[1] != second_network.partition[0]: + raise_error( + ValueError, + "Systems of the channel do not match the super-channel: " + + f"{self.partition[1], self.partition[2]} != " + + f"{second_network.partition[0],second_network.partition[1]}.", + ) + + subscripts = "jklm,kl -> jm" else: raise_error( NotImplementedError, @@ -593,8 +631,7 @@ def _run_checks(self, partition, system_output, pure): def _set_tensor_and_parameters(self): """Sets tensor based on inputs.""" - if self._backend is None: - self._backend = GlobalBackend() + self._backend = _check_backend(self._backend) if isinstance(self.partition, list): self.partition = tuple(self.partition) @@ -625,7 +662,7 @@ def _set_tensor_and_parameters(self): def _full(self): """Reshapes input matrix based on purity.""" matrix = np.copy(self._matrix) - if self.pure(): + if self.is_pure(): matrix = np.einsum("jk,lm -> kjml", matrix, np.conj(matrix)) return matrix @@ -636,7 +673,7 @@ def _check_subscript_pattern(self, subscripts: str): """Checks if input subscript match any implemented pattern.""" braket = "[a-z]" pattern_two = re.compile(braket * 2 + "," + braket * 2 + "->" + braket * 2) - pattern_four = re.compile(braket * 4 + "," + braket * 4 + "->" + braket * 4) + pattern_four = re.compile(braket * 4 + "," + braket * 2 + "->" + braket * 2) return bool(re.match(pattern_two, subscripts)), bool( re.match(pattern_four, subscripts) diff --git a/src/qibo/quantum_info/random_ensembles.py b/src/qibo/quantum_info/random_ensembles.py index 84cb79af48..94159cf1b2 100644 --- a/src/qibo/quantum_info/random_ensembles.py +++ b/src/qibo/quantum_info/random_ensembles.py @@ -7,7 +7,7 @@ from scipy.stats import rv_continuous from qibo import Circuit, gates -from qibo.backends import GlobalBackend, NumpyBackend +from qibo.backends import NumpyBackend, _check_backend from qibo.config import MAX_ITERATIONS, PRECISION_TOL, raise_error from qibo.quantum_info.basis import comp_basis_to_pauli from qibo.quantum_info.superoperator_transformations import ( @@ -32,10 +32,10 @@ def _ppf(self, theta: float): def uniform_sampling_U3(ngates: int, seed=None, backend=None): - """Samples parameters for Haar-random :math:`U_{3}`s (:class:`qibo.gates.U3`). + """Samples parameters for Haar-random :class:`qibo.gates.U3`. Args: - ngates (int): Total number of :math:`U_{3}`s to be sampled. + ngates (int): Total number of :math:`U_{3}` gates to be sampled. seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random numbers or a fixed seed to initialize a generator. If ``None``, initializes a generator with a random seed. Default: ``None``. @@ -44,7 +44,7 @@ def uniform_sampling_U3(ngates: int, seed=None, backend=None): Defaults to ``None``. Returns: - (ndarray): array of shape (``ngates``, :math:`3`). + ndarray: array of shape (``ngates``, :math:`3`). """ if not isinstance(ngates, int): raise_error( @@ -384,7 +384,7 @@ def random_quantum_channel( return super_op -def random_statevector(dims: int, haar: bool = False, seed=None, backend=None): +def random_statevector(dims: int, seed=None, backend=None): """Creates a random statevector :math:`\\ket{\\psi}`. .. math:: @@ -396,10 +396,6 @@ def random_statevector(dims: int, haar: bool = False, seed=None, backend=None): Args: dims (int): dimension of the matrix. - haar (bool, optional): if ``True``, statevector is created by sampling a - Haar random unitary :math:`U_{\\text{haar}}` and acting with it on a - random computational basis state :math:`\\ket{k}`, i.e. - :math:`\\ket{\\psi} = U_{\\text{haar}} \\ket{k}`. Defaults to ``False``. seed (int or :class:`numpy.random.Generator`, optional): Either a generator of random numbers or a fixed seed to initialize a generator. If ``None``, initializes a generator with a random seed. Defaults to ``None``. @@ -414,9 +410,6 @@ def random_statevector(dims: int, haar: bool = False, seed=None, backend=None): if dims <= 0: raise_error(ValueError, "dim must be of type int and >= 1") - if not isinstance(haar, bool): - raise_error(TypeError, f"haar must be type bool, but it is type {type(haar)}.") - if ( seed is not None and not isinstance(seed, int) @@ -426,23 +419,12 @@ def random_statevector(dims: int, haar: bool = False, seed=None, backend=None): TypeError, "seed must be either type int or numpy.random.Generator." ) - if backend is None: # pragma: no cover - backend = GlobalBackend() - - local_state = ( - np.random.default_rng(seed) if seed is None or isinstance(seed, int) else seed - ) + backend, local_state = _set_backend_and_local_state(seed, backend) - if not haar: - # sample real and imag parts of complex amplitude in [-1, 1] - state = 1j * (2 * local_state.random(dims) - 1) - state += 2 * local_state.random(dims) - 1 - state /= np.linalg.norm(state) - state = backend.cast(state, dtype=state.dtype) - else: - # select a random column of a haar random unitary - k = local_state.integers(low=0, high=dims) - state = random_unitary(dims, measure="haar", seed=seed, backend=backend)[:, k] + state = local_state.standard_normal(dims).astype(complex) + state += 1.0j * local_state.standard_normal(dims) + state /= np.linalg.norm(state) + state = backend.cast(state, dtype=state.dtype) return state @@ -566,7 +548,7 @@ def random_density_matrix( random_gaussian_matrix(dims, rank, seed=local_state, backend=backend), ) state = np.dot(state, np.transpose(np.conj(state))) - state = state / np.trace(state) + state /= np.trace(state) state = backend.cast(state, dtype=state.dtype) @@ -597,7 +579,7 @@ def random_clifford( Args: nqubits (int): number of qubits. return_circuit (bool, optional): if ``True``, returns a :class:`qibo.models.Circuit` - object. If ``False``, returns an ``ndarray`` object. Defaults to ``False``. + object. If ``False``, returns an ``ndarray`` object. Defaults to ``True``. density_matrix (bool, optional): used when ``return_circuit=True``. If `True`, the circuit would evolve density matrices. Defaults to ``False``. seed (int or :class:`numpy.random.Generator`, optional): Either a generator of @@ -1219,8 +1201,7 @@ def _set_backend_and_local_state(seed, backend): TypeError, "seed must be either type int or numpy.random.Generator." ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if seed is None or isinstance(seed, int): if backend.__class__.__name__ in [ diff --git a/src/qibo/quantum_info/superoperator_transformations.py b/src/qibo/quantum_info/superoperator_transformations.py index b91dadbeb1..c888ed5b18 100644 --- a/src/qibo/quantum_info/superoperator_transformations.py +++ b/src/qibo/quantum_info/superoperator_transformations.py @@ -6,7 +6,7 @@ import numpy as np from scipy.optimize import minimize -from qibo.backends import GlobalBackend +from qibo.backends import _check_backend from qibo.config import PRECISION_TOL, raise_error from qibo.gates.abstract import Gate from qibo.gates.gates import Unitary @@ -61,8 +61,7 @@ def vectorization(state, order: str = "row", backend=None): f"order must be either 'row' or 'column' or 'system', but it is {order}.", ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if len(state.shape) == 1: state = np.outer(state, np.conj(state)) @@ -130,8 +129,7 @@ def unvectorization(state, order: str = "row", backend=None): f"order must be either 'row' or 'column' or 'system', but it is {order}.", ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) dim = int(np.sqrt(len(state))) @@ -449,8 +447,7 @@ def choi_to_kraus( f"validate_cp must be type bool, but it is type {type(validate_cp)}.", ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if validate_cp: norm = float( @@ -652,8 +649,7 @@ def kraus_to_choi(kraus_ops, order: str = "row", backend=None): Returns: ndarray: Choi representation of the Kraus channel. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) gates, target_qubits = _set_gate_and_target_qubits(kraus_ops) nqubits = 1 + max(target_qubits) @@ -782,8 +778,7 @@ def kraus_to_chi( """ from qibo.quantum_info.basis import comp_basis_to_pauli - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) gates, target_qubits = _set_gate_and_target_qubits(kraus_ops) nqubits = 1 + max(target_qubits) @@ -845,8 +840,7 @@ def kraus_to_stinespring( Returns: ndarray: Stinespring representation (restricted unitary) of the Kraus channel. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if initial_state_env is not None: if len(initial_state_env) != len(kraus_ops): @@ -963,8 +957,7 @@ def liouville_to_pauli( """ from qibo.quantum_info.basis import comp_basis_to_pauli - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) dim = int(np.sqrt(len(super_op))) nqubits = int(np.log2(dim)) @@ -1165,8 +1158,7 @@ def pauli_to_liouville( """ from qibo.quantum_info.basis import pauli_to_comp_basis - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) dim = int(np.sqrt(len(pauli_op))) nqubits = int(np.log2(dim)) @@ -1873,8 +1865,7 @@ def stinespring_to_kraus( Returns: ndarray: Kraus operators. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if isinstance(dim_env, int) is False: raise_error( @@ -2038,8 +2029,7 @@ def kraus_to_unitaries( if precision_tol < 0.0: raise_error(ValueError, "precision_tol must be non-negative.") - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) target_qubits = [q for q, _ in kraus_ops] nqubits = 1 + np.max(target_qubits) @@ -2148,8 +2138,7 @@ def _reshuffling(super_op, order: str = "row", backend=None): NotImplementedError, "reshuffling not implemented for system vectorization." ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) dim = np.sqrt(super_op.shape[0]) @@ -2215,8 +2204,7 @@ def _individual_kraus_to_liouville( to be used in :func:`qibo.quantum_info.kraus_to_unitaries`. In principle, this should be not be accessible to users. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) gates, target_qubits = _set_gate_and_target_qubits(kraus_ops) nqubits = 1 + max(target_qubits) diff --git a/src/qibo/quantum_info/utils.py b/src/qibo/quantum_info/utils.py index 24a6998569..ec772168e0 100644 --- a/src/qibo/quantum_info/utils.py +++ b/src/qibo/quantum_info/utils.py @@ -9,7 +9,7 @@ import numpy as np from qibo import matrices -from qibo.backends import GlobalBackend +from qibo.backends import _check_backend from qibo.config import PRECISION_TOL, raise_error @@ -134,8 +134,7 @@ def hadamard_transform(array, implementation: str = "fast", backend=None): Returns: ndarray: (Fast) Hadamard Transform of ``array``. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if ( len(array.shape) not in [1, 2] @@ -190,134 +189,6 @@ def hadamard_transform(array, implementation: str = "fast", backend=None): return array -def shannon_entropy(probability_array, base: float = 2, backend=None): - """Calculates the Shannon entropy of a probability array :math:`\\mathbf{p}`, which is given by - - .. math:: - H(\\mathbf{p}) = - \\sum_{k = 0}^{d^{2} - 1} \\, p_{k} \\, \\log_{b}(p_{k}) \\, , - - where :math:`d = \\text{dim}(\\mathcal{H})` is the dimension of the - Hilbert space :math:`\\mathcal{H}`, :math:`b` is the log base (default 2), - and :math:`0 \\log_{b}(0) \\equiv 0`. - - Args: - probability_array (ndarray or list): a probability array :math:`\\mathbf{p}`. - base (float): the base of the log. Defaults to :math:`2`. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be used - in the execution. If ``None``, it uses :class:`qibo.backends.GlobalBackend`. - Defaults to ``None``. - - Returns: - (float): The Shannon entropy :math:`H(\\mathcal{p})`. - """ - if backend is None: # pragma: no cover - backend = GlobalBackend() - - if isinstance(probability_array, list): - probability_array = backend.cast(probability_array, dtype=np.float64) - - if base <= 0: - raise_error(ValueError, "log base must be non-negative.") - - if len(probability_array.shape) != 1: - raise_error( - TypeError, - f"Probability array must have dims (k,) but it has {probability_array.shape}.", - ) - - if len(probability_array) == 0: - raise_error(TypeError, "Empty array.") - - if any(probability_array < 0) or any(probability_array > 1.0): - raise_error( - ValueError, - "All elements of the probability array must be between 0. and 1..", - ) - - if np.abs(np.sum(probability_array) - 1.0) > PRECISION_TOL: - raise_error(ValueError, "Probability array must sum to 1.") - - if base == 2: - log_prob = np.where(probability_array != 0.0, np.log2(probability_array), 0.0) - elif base == 10: - log_prob = np.where(probability_array != 0, np.log10(probability_array), 0.0) - elif base == np.e: - log_prob = np.where(probability_array != 0, np.log(probability_array), 0.0) - else: - log_prob = np.where( - probability_array != 0, np.log(probability_array) / np.log(base), 0.0 - ) - - entropy = -np.sum(probability_array * log_prob) - - # absolute value if entropy == 0.0 to avoid returning -0.0 - entropy = np.abs(entropy) if entropy == 0.0 else entropy - - return complex(entropy).real - - -def total_variation_distance( - prob_dist_p, prob_dist_q, validate: bool = False, backend=None -): - """Calculates the Total Variation (TV) distance between two discrete probability distributions. - - For probabilities :math:`\\mathbf{p}` and :math:`\\mathbf{q}`, it is defined as - - .. math:: - d_{\\text{TV}}(\\mathbf{p} \\, , \\, \\mathbf{q}) = \\frac{1}{2} - \\, \\| \\mathbf{p} - \\mathbf{q} \\|_{1} \\, , - - where :math:`\\| \\cdot \\|_{1}` is the :math:`\\mathcal{l}_{1}`-norm. - - Args: - prob_dist_p (ndarray or list): discrete probability distribution :math:`p`. - prob_dist_q (ndarray or list): discrete probability distribution :math:`q`. - validate (bool, optional): If ``True``, checks if :math:`p` and :math:`q` are proper - probability distributions. Defaults to ``False``. - backend (:class:`qibo.backends.abstract.Backend`, optional): backend to be - used in the execution. If ``None``, it uses - :class:`qibo.backends.GlobalBackend`. Defaults to ``None``. - - Returns: - float: Total variation distance between :math:`\\mathbf{p}` and :math:`\\mathbf{q}`. - """ - if backend is None: # pragma: no cover - backend = GlobalBackend() - - if isinstance(prob_dist_p, list): - prob_dist_p = backend.cast(prob_dist_p, dtype=np.float64) - if isinstance(prob_dist_q, list): - prob_dist_q = backend.cast(prob_dist_q, dtype=np.float64) - - if (len(prob_dist_p.shape) != 1) or (len(prob_dist_q.shape) != 1): - raise_error( - TypeError, - "Probability arrays must have dims (k,) but have " - + f"dims {prob_dist_p.shape} and {prob_dist_q.shape}.", - ) - - if (len(prob_dist_p) == 0) or (len(prob_dist_q) == 0): - raise_error(TypeError, "At least one of the arrays is empty.") - - if validate: - if (any(prob_dist_p < 0) or any(prob_dist_p > 1.0)) or ( - any(prob_dist_q < 0) or any(prob_dist_q > 1.0) - ): - raise_error( - ValueError, - "All elements of the probability array must be between 0. and 1..", - ) - if np.abs(np.sum(prob_dist_p) - 1.0) > PRECISION_TOL: - raise_error(ValueError, "First probability array must sum to 1.") - - if np.abs(np.sum(prob_dist_q) - 1.0) > PRECISION_TOL: - raise_error(ValueError, "Second probability array must sum to 1.") - - total_variation = 0.5 * np.sum(np.abs(prob_dist_p - prob_dist_q)) - - return total_variation - - def hellinger_distance(prob_dist_p, prob_dist_q, validate: bool = False, backend=None): """Calculates the Hellinger distance :math:`H` between two discrete probability distributions. @@ -341,8 +212,7 @@ def hellinger_distance(prob_dist_p, prob_dist_q, validate: bool = False, backend Returns: (float): Hellinger distance :math:`H(p, q)`. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if isinstance(prob_dist_p, list): prob_dist_p = backend.cast(prob_dist_p, dtype=np.float64) @@ -454,8 +324,7 @@ def haar_integral( TypeError, f"samples must be type int, but it is type {type(samples)}." ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) dim = 2**nqubits @@ -469,9 +338,7 @@ def haar_integral( rand_unit_density, dtype=rand_unit_density.dtype ) for _ in range(samples): - haar_state = np.reshape( - random_statevector(dim, haar=True, backend=backend), (-1, 1) - ) + haar_state = np.reshape(random_statevector(dim, backend=backend), (-1, 1)) rho = haar_state @ np.conj(np.transpose(haar_state)) @@ -531,8 +398,7 @@ def pqc_integral(circuit, power_t: int, samples: int, backend=None): TypeError, f"samples must be type int, but it is type {type(samples)}." ) - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) circuit.density_matrix = True dim = 2**circuit.nqubits diff --git a/src/qibo/result.py b/src/qibo/result.py index be095d5bfa..5d74157007 100644 --- a/src/qibo/result.py +++ b/src/qibo/result.py @@ -31,11 +31,9 @@ class QuantumState: """ def __init__(self, state, backend=None): - if backend is None: # pragma: no cover - from qibo.backends import GlobalBackend + from qibo.backends import _check_backend - backend = GlobalBackend() - self.backend = backend + self.backend = _check_backend(backend) self.density_matrix = len(state.shape) == 2 self.nqubits = int(np.log2(state.shape[0])) self._state = state diff --git a/src/qibo/transpiler/__init__.py b/src/qibo/transpiler/__init__.py index 3ed3779d69..bfe576776c 100644 --- a/src/qibo/transpiler/__init__.py +++ b/src/qibo/transpiler/__init__.py @@ -1,6 +1,12 @@ from qibo.transpiler.optimizer import Preprocessing, Rearrange from qibo.transpiler.pipeline import Passes -from qibo.transpiler.placer import Custom, Random, ReverseTraversal, Subgraph, Trivial -from qibo.transpiler.router import Sabre, ShortestPaths -from qibo.transpiler.star_connectivity import StarConnectivity +from qibo.transpiler.placer import ( + Custom, + Random, + ReverseTraversal, + StarConnectivityPlacer, + Subgraph, + Trivial, +) +from qibo.transpiler.router import Sabre, ShortestPaths, StarConnectivityRouter from qibo.transpiler.unroller import NativeGates diff --git a/src/qibo/transpiler/blocks.py b/src/qibo/transpiler/blocks.py index 199979767e..6e2b1122a2 100644 --- a/src/qibo/transpiler/blocks.py +++ b/src/qibo/transpiler/blocks.py @@ -200,12 +200,11 @@ def block_decomposition(circuit: Circuit, fuse: bool = True): remove_list = [first_block] if len(initial_blocks[1:]) > 0: for second_block in initial_blocks[1:]: - try: + if second_block.qubits == first_block.qubits: first_block = first_block.fuse(second_block) remove_list.append(second_block) - except BlockingError: - if not first_block.commute(second_block): - break + elif not first_block.commute(second_block): + break blocks.append(first_block) _remove_gates(initial_blocks, remove_list) diff --git a/src/qibo/transpiler/decompositions.py b/src/qibo/transpiler/decompositions.py index cb106d423b..f559f0088a 100644 --- a/src/qibo/transpiler/decompositions.py +++ b/src/qibo/transpiler/decompositions.py @@ -116,7 +116,7 @@ def _u3_to_gpi2(t, p, l): # Decompose single qubit gates using U3 u3_dec = GateDecompositions() -u3_dec.add(gates.H, [gates.U3(0, 7 * np.pi / 2, np.pi, 0)]) +u3_dec.add(gates.H, [gates.U3(0, -np.pi / 2, np.pi, 0)]) u3_dec.add(gates.X, [gates.U3(0, np.pi, 0, np.pi)]) u3_dec.add(gates.Y, [gates.U3(0, np.pi, 0, 0)]) u3_dec.add(gates.Z, [gates.Z(0)]) @@ -159,28 +159,28 @@ def _u3_to_gpi2(t, p, l): gates.CNOT, [ gates.U3(0, 3 * np.pi / 2, np.pi, 0), - gates.U3(1, np.pi / 2, -np.pi, -np.pi), + gates.U3(1, np.pi / 2, np.pi, np.pi), gates.iSWAP(0, 1), gates.U3(0, np.pi, 0, np.pi), - gates.U3(1, np.pi / 2, -np.pi, -np.pi), + gates.U3(1, np.pi / 2, np.pi, np.pi), gates.iSWAP(0, 1), - gates.U3(0, np.pi / 2, np.pi / 2, -np.pi), - gates.U3(1, np.pi / 2, -np.pi, -np.pi / 2), + gates.U3(0, np.pi / 2, np.pi / 2, np.pi), + gates.U3(1, np.pi / 2, np.pi, -np.pi / 2), ], ) iswap_dec.add( gates.CZ, [ - gates.U3(0, 7 * np.pi / 2, np.pi, 0), - gates.U3(1, 7 * np.pi / 2, np.pi, 0), - gates.U3(1, np.pi / 2, -np.pi, -np.pi), + gates.U3(0, -np.pi / 2, np.pi, 0), + gates.U3(1, -np.pi / 2, np.pi, 0), + gates.U3(1, np.pi / 2, np.pi, np.pi), gates.iSWAP(0, 1), gates.U3(0, np.pi, 0, np.pi), - gates.U3(1, np.pi / 2, -np.pi, -np.pi), + gates.U3(1, np.pi / 2, np.pi, np.pi), gates.iSWAP(0, 1), - gates.U3(0, np.pi / 2, np.pi / 2, -np.pi), - gates.U3(1, np.pi / 2, -np.pi, -np.pi / 2), - gates.U3(1, 7 * np.pi / 2, np.pi, 0), + gates.U3(0, np.pi / 2, np.pi / 2, np.pi), + gates.U3(1, np.pi / 2, np.pi, -np.pi / 2), + gates.U3(1, -np.pi / 2, np.pi, 0), ], ) iswap_dec.add( @@ -304,14 +304,14 @@ def _u3_to_gpi2(t, p, l): cz_dec.add( gates.FSWAP, [ - gates.U3(0, np.pi / 2, -np.pi / 2, -np.pi), + gates.U3(0, np.pi / 2, -np.pi / 2, np.pi), gates.U3(1, np.pi / 2, np.pi / 2, np.pi / 2), gates.CZ(0, 1), gates.U3(0, np.pi / 2, 0, -np.pi / 2), gates.U3(1, np.pi / 2, 0, np.pi / 2), gates.CZ(0, 1), - gates.U3(0, np.pi / 2, np.pi / 2, -np.pi), - gates.U3(1, np.pi / 2, 0, -np.pi), + gates.U3(0, np.pi / 2, np.pi / 2, np.pi), + gates.U3(1, np.pi / 2, 0, np.pi), ], ) cz_dec.add( @@ -328,7 +328,7 @@ def _u3_to_gpi2(t, p, l): gates.RYY, lambda gate: [ gates.RX(0, np.pi / 2), - gates.U3(1, np.pi / 2, np.pi / 2, -np.pi), + gates.U3(1, np.pi / 2, np.pi / 2, np.pi), gates.CZ(0, 1), gates.RX(1, gate.parameters[0]), gates.CZ(0, 1), @@ -392,3 +392,76 @@ def _u3_to_gpi2(t, p, l): gates.H(1), ], ) + + +# standard gate decompositions used by :meth:`qibo.gates.gates.Gate.decompose` +standard_decompositions = GateDecompositions() +standard_decompositions.add(gates.SX, [gates.RX(0, np.pi / 2, trainable=False)]) +standard_decompositions.add(gates.SXDG, [gates.RX(0, -np.pi / 2, trainable=False)]) +standard_decompositions.add( + gates.U3, + lambda gate: [ + gates.RZ(0, gate.parameters[2]), + gates.SX(0), + gates.RZ(0, gate.parameters[0] + np.pi), + gates.SX(0), + gates.RZ(0, gate.parameters[1] + np.pi), + ], +) +standard_decompositions.add(gates.CY, [gates.SDG(1), gates.CNOT(0, 1), gates.S(1)]) +standard_decompositions.add(gates.CZ, [gates.H(1), gates.CNOT(0, 1), gates.H(1)]) +standard_decompositions.add( + gates.CSX, [gates.H(1), gates.CU1(0, 1, np.pi / 2), gates.H(1)] +) +standard_decompositions.add( + gates.CSXDG, [gates.H(1), gates.CU1(0, 1, -np.pi / 2), gates.H(1)] +) +standard_decompositions.add( + gates.RZX, + lambda gate: [ + gates.H(1), + gates.CNOT(0, 1), + gates.RZ(1, gate.parameters[0]), + gates.CNOT(0, 1), + gates.H(1), + ], +) +standard_decompositions.add( + gates.RXXYY, + lambda gate: [ + gates.RZ(1, -np.pi / 2), + gates.S(0), + gates.SX(1), + gates.RZ(1, np.pi / 2), + gates.CNOT(1, 0), + gates.RY(0, -gate.parameters[0] / 2), + gates.RY(1, -gate.parameters[0] / 2), + gates.CNOT(1, 0), + gates.SDG(0), + gates.RZ(1, -np.pi / 2), + gates.SX(1).dagger(), + gates.RZ(1, np.pi / 2), + ], +) +standard_decompositions.add( + gates.RBS, + lambda gate: [ + gates.H(0), + gates.CNOT(0, 1), + gates.H(1), + gates.RY(0, gate.parameters[0]), + gates.RY(1, -gate.parameters[0]), + gates.H(1), + gates.CNOT(0, 1), + gates.H(0), + ], +) +standard_decompositions.add( + gates.GIVENS, lambda gate: gates.RBS(0, 1, -gate.parameters[0]).decompose() +) +standard_decompositions.add( + gates.FSWAP, [gates.X(1)] + gates.GIVENS(0, 1, np.pi / 2).decompose() + [gates.X(0)] +) +standard_decompositions.add( + gates.ECR, [gates.S(0), gates.SX(1), gates.CNOT(0, 1), gates.X(0)] +) diff --git a/src/qibo/transpiler/pipeline.py b/src/qibo/transpiler/pipeline.py index d1bd6fdc6c..06b047d773 100644 --- a/src/qibo/transpiler/pipeline.py +++ b/src/qibo/transpiler/pipeline.py @@ -10,9 +10,12 @@ from qibo.transpiler._exceptions import TranspilerPipelineError from qibo.transpiler.abstract import Optimizer, Placer, Router from qibo.transpiler.optimizer import Preprocessing -from qibo.transpiler.placer import Trivial, assert_placement -from qibo.transpiler.router import ConnectivityError, assert_connectivity -from qibo.transpiler.star_connectivity import StarConnectivity +from qibo.transpiler.placer import StarConnectivityPlacer, Trivial, assert_placement +from qibo.transpiler.router import ( + ConnectivityError, + StarConnectivityRouter, + assert_connectivity, +) from qibo.transpiler.unroller import ( DecompositionError, NativeGates, @@ -203,9 +206,9 @@ def default(self): # preprocessing default_passes.append(Preprocessing(connectivity=self.connectivity)) # default placer pass - default_passes.append(Trivial(connectivity=self.connectivity)) + default_passes.append(StarConnectivityPlacer()) # default router pass - default_passes.append(StarConnectivity()) + default_passes.append(StarConnectivityRouter()) # default unroller pass default_passes.append(Unroller(native_gates=self.native_gates)) diff --git a/src/qibo/transpiler/placer.py b/src/qibo/transpiler/placer.py index 96c5ae04d0..d57cc95428 100644 --- a/src/qibo/transpiler/placer.py +++ b/src/qibo/transpiler/placer.py @@ -4,10 +4,11 @@ import networkx as nx from qibo import gates -from qibo.config import raise_error +from qibo.config import log, raise_error from qibo.models import Circuit from qibo.transpiler._exceptions import PlacementError from qibo.transpiler.abstract import Placer, Router +from qibo.transpiler.router import _find_connected_qubit def assert_placement( @@ -84,6 +85,66 @@ def _find_gates_qubits_pairs(circuit: Circuit): return gates_qubits_pairs +class StarConnectivityPlacer(Placer): + """Find an optimized qubit placement for the following connectivity: + + q + | + q -- q -- q + | + q + + Args: + connectivity (:class:`networkx.Graph`): chip connectivity, not used for this transpiler. + middle_qubit (int, optional): qubit id of the qubit that is in the middle of the star. + """ + + def __init__(self, connectivity=None, middle_qubit: int = 2): + self.middle_qubit = middle_qubit + if connectivity is not None: # pragma: no cover + log.warning( + "StarConnectivityRouter does not use the connectivity graph." + "The connectivity graph will be ignored." + ) + + def __call__(self, circuit: Circuit): + """Apply the transpiler transformation on a given circuit. + + Args: + circuit (:class:`qibo.models.circuit.Circuit`): The original Qibo circuit to transform. + Only single qubit gates and two qubits gates are supported by the router. + + Returns: + (dict): physical to logical qubit mapping. + """ + + # find the number of qubits for hardware circuit + nqubits = max(circuit.nqubits, self.middle_qubit + 1) + hardware_qubits = list(range(nqubits)) + + for i, gate in enumerate(circuit.queue): + if len(gate.qubits) > 2: + raise_error( + PlacementError, + "Gates targeting more than 2 qubits are not supported", + ) + if len(gate.qubits) == 2: + if self.middle_qubit not in gate.qubits: + new_middle = _find_connected_qubit( + gate.qubits, + circuit.queue[i + 1 :], + hardware_qubits, + error=PlacementError, + ) + hardware_qubits[self.middle_qubit], hardware_qubits[new_middle] = ( + new_middle, + self.middle_qubit, + ) + break + + return dict(zip(["q" + str(i) for i in range(nqubits)], hardware_qubits)) + + class Trivial(Placer): """Place qubits according to the following notation: diff --git a/src/qibo/transpiler/router.py b/src/qibo/transpiler/router.py index 00e6a86da5..6023296d8c 100644 --- a/src/qibo/transpiler/router.py +++ b/src/qibo/transpiler/router.py @@ -5,7 +5,7 @@ import networkx as nx from qibo import gates -from qibo.config import raise_error +from qibo.config import log, raise_error from qibo.models import Circuit from qibo.transpiler._exceptions import ConnectivityError from qibo.transpiler.abstract import Router @@ -40,6 +40,119 @@ def assert_connectivity(connectivity: nx.Graph, circuit: Circuit): ) +class StarConnectivityRouter(Router): + """Transforms an arbitrary circuit to one that can be executed on hardware. + + This transpiler produces a circuit that respects the following connectivity: + + q + | + q -- q -- q + | + q + + by adding SWAP gates when needed. + + Args: + connectivity (:class:`networkx.Graph`): chip connectivity, not used for this transpiler. + middle_qubit (int, optional): qubit id of the qubit that is in the middle of the star. + """ + + def __init__(self, connectivity=None, middle_qubit: int = 2): + self.middle_qubit = middle_qubit + if connectivity is not None: # pragma: no cover + log.warning( + "StarConnectivityRouter does not use the connectivity graph." + "The connectivity graph will be ignored." + ) + + def __call__(self, circuit: Circuit, initial_layout: dict): + """Apply the transpiler transformation on a given circuit. + + Args: + circuit (:class:`qibo.models.circuit.Circuit`): The original Qibo circuit to transform. + Only single qubit gates and two qubits gates are supported by the router. + initial_layout (dict): initial physical-to-logical qubit mapping, + use `qibo.transpiler.placer.StarConnectivityPlacer` for better performance. + + Returns: + (:class:`qibo.models.circuit.Circuit`, list): circuit that performs the same operation + as the original but respects the hardware connectivity, + and list that maps logical to hardware qubits. + """ + + middle_qubit = self.middle_qubit + nqubits = max(circuit.nqubits, middle_qubit + 1) + # new circuit object that will be compatible with hardware connectivity + new = Circuit(nqubits) + # list to maps logical to hardware qubits + hardware_qubits = list(initial_layout.values()) + + for i, gate in enumerate(circuit.queue): + # map gate qubits to hardware + qubits = tuple(hardware_qubits.index(q) for q in gate.qubits) + if isinstance(gate, gates.M): + new_gate = gates.M(*qubits, **gate.init_kwargs) + new_gate.result = gate.result + new.add(new_gate) + continue + + if len(qubits) > 2: + raise ConnectivityError( + "Gates targeting more than two qubits are not supported." + ) + + elif len(qubits) == 2 and middle_qubit not in qubits: + # find which qubit should be moved + new_middle = _find_connected_qubit( + qubits, + circuit.queue[i + 1 :], + hardware_qubits, + error=ConnectivityError, + ) + # update hardware qubits according to the swap + hardware_qubits[middle_qubit], hardware_qubits[new_middle] = ( + hardware_qubits[new_middle], + hardware_qubits[middle_qubit], + ) + new.add(gates.SWAP(middle_qubit, new_middle)) + # update gate qubits according to the new swap + qubits = tuple(hardware_qubits.index(q) for q in gate.qubits) + + # add gate to the hardware circuit + if isinstance(gate, gates.Unitary): + # gates.Unitary requires matrix as first argument + matrix = gate.init_args[0] + new.add(gate.__class__(matrix, *qubits, **gate.init_kwargs)) + else: + new.add(gate.__class__(*qubits, **gate.init_kwargs)) + hardware_qubits_keys = ["q" + str(i) for i in range(5)] + return new, dict(zip(hardware_qubits_keys, hardware_qubits)) + + +def _find_connected_qubit(qubits, queue, hardware_qubits, error): + """Helper method for :meth:`qibo.transpiler.router.StarConnectivityRouter` + and :meth:`qibo.transpiler.router.StarConnectivityPlacer`. + + Finds which qubit should be mapped to hardware middle qubit + by looking at the two-qubit gates that follow. + """ + possible_qubits = set(qubits) + for next_gate in queue: + if len(next_gate.qubits) > 2: + raise_error( + error, + "Gates targeting more than 2 qubits are not supported", + ) + if len(next_gate.qubits) == 2: + possible_qubits &= {hardware_qubits.index(q) for q in next_gate.qubits} + if not possible_qubits: + return qubits[0] + elif len(possible_qubits) == 1: + return possible_qubits.pop() + return qubits[0] + + class CircuitMap: """Class that stores the circuit and physical-logical mapping during routing. @@ -266,7 +379,9 @@ def __call__(self, circuit: Circuit, initial_layout: dict): else: self._find_new_mapping() - routed_circuit = self.circuit.routed_circuit(circuit_kwargs=circuit.init_kwargs) + circuit_kwargs = circuit.init_kwargs + circuit_kwargs["wire_names"] = list(initial_layout.keys()) + routed_circuit = self.circuit.routed_circuit(circuit_kwargs=circuit_kwargs) if self._final_measurements is not None: routed_circuit = self._append_final_measurements( routed_circuit=routed_circuit @@ -545,7 +660,9 @@ def __call__(self, circuit: Circuit, initial_layout: dict): else: self._find_new_mapping() - routed_circuit = self.circuit.routed_circuit(circuit_kwargs=circuit.init_kwargs) + circuit_kwargs = circuit.init_kwargs + circuit_kwargs["wire_names"] = list(initial_layout.keys()) + routed_circuit = self.circuit.routed_circuit(circuit_kwargs=circuit_kwargs) if self._final_measurements is not None: routed_circuit = self._append_final_measurements( routed_circuit=routed_circuit diff --git a/src/qibo/transpiler/star_connectivity.py b/src/qibo/transpiler/star_connectivity.py deleted file mode 100644 index 693df030c1..0000000000 --- a/src/qibo/transpiler/star_connectivity.py +++ /dev/null @@ -1,126 +0,0 @@ -from qibo import Circuit, gates -from qibo.transpiler.abstract import Router -from qibo.transpiler.router import ConnectivityError - - -# TODO: split into routing plus placer steps -class StarConnectivity(Router): - """Transforms an arbitrary circuit to one that can be executed on hardware. - - This transpiler produces a circuit that respects the following connectivity: - - q - | - q -- q -- q - | - q - - by adding SWAP gates when needed. - - Args: - connectivity (:class:`networkx.Graph`): chip connectivity, not used for this transpiler. - middle_qubit (int, optional): qubit id of the qubit that is in the middle of the star. - """ - - def __init__(self, connectivity=None, middle_qubit: int = 2): - self.middle_qubit = middle_qubit - self.connectivity = connectivity - - def __call__(self, circuit: Circuit, initial_layout=None): - """Apply the transpiler transformation on a given circuit. - - Args: - circuit (:class:`qibo.models.circuit.Circuit`): The original Qibo circuit to transform. - This circuit must contain up to two-qubit gates. - - Returns: - (:class:`qibo.models.circuit.Circuit`, list): circuit that performs the same operation - as the original but respects the hardware connectivity, - and list that maps logical to hardware qubits. - """ - - middle_qubit = self.middle_qubit - # find the number of qubits for hardware circuit - if circuit.nqubits == 1: - nqubits = 1 - else: - nqubits = max(circuit.nqubits, middle_qubit + 1) - # new circuit object that will be compatible with hardware connectivity - new = circuit.__class__(nqubits) - # list to maps logical to hardware qubits - hardware_qubits = list(range(nqubits)) - - # find initial qubit mapping - for i, gate in enumerate(circuit.queue): - if len(gate.qubits) == 2: - if middle_qubit not in gate.qubits: - new_middle = _find_connected_qubit( - gate.qubits, circuit.queue[i + 1 :], hardware_qubits - ) - hardware_qubits[middle_qubit], hardware_qubits[new_middle] = ( - hardware_qubits[new_middle], - hardware_qubits[middle_qubit], - ) - break - - # the first SWAP is not needed as it can be applied via virtual mapping - add_swap = False - for i, gate in enumerate(circuit.queue): - # map gate qubits to hardware - qubits = tuple(hardware_qubits.index(q) for q in gate.qubits) - if isinstance(gate, gates.M): - new_gate = gates.M(*qubits, **gate.init_kwargs) - new_gate.result = gate.result - new.add(new_gate) - continue - - if len(qubits) > 2: - raise ConnectivityError( - "Gates targeting more than two qubits are not supported." - ) - - elif len(qubits) == 2 and middle_qubit not in qubits: - # find which qubit should be moved to 0 - new_middle = _find_connected_qubit( - qubits, circuit.queue[i + 1 :], hardware_qubits - ) - # update hardware qubits according to the swap - hardware_qubits[middle_qubit], hardware_qubits[new_middle] = ( - hardware_qubits[new_middle], - hardware_qubits[middle_qubit], - ) - if add_swap: - new.add(gates.SWAP(middle_qubit, new_middle)) - # update gate qubits according to the new swap - qubits = tuple(hardware_qubits.index(q) for q in gate.qubits) - - # add gate to the hardware circuit - if isinstance(gate, gates.Unitary): - # gates.Unitary requires matrix as first argument - matrix = gate.init_args[0] - new.add(gate.__class__(matrix, *qubits, **gate.init_kwargs)) - else: - new.add(gate.__class__(*qubits, **gate.init_kwargs)) - if len(qubits) == 2: - add_swap = True - hardware_qubits_keys = ["q" + str(i) for i in range(5)] - return new, dict(zip(hardware_qubits_keys, hardware_qubits)) - - -def _find_connected_qubit(qubits, queue, hardware_qubits): - """Helper method for :meth:`qibo.transpiler.StarConnectivity`. - - Finds which qubit should be mapped to hardware middle qubit - by looking at the two-qubit gates that follow. - """ - possible_qubits = set(qubits) - for next_gate in queue: - if len(next_gate.qubits) == 2: - possible_qubits &= {hardware_qubits.index(q) for q in next_gate.qubits} - if not possible_qubits: - # freedom of choice - return qubits[0] - elif len(possible_qubits) == 1: - return possible_qubits.pop() - # freedom of choice - return qubits[0] diff --git a/src/qibo/transpiler/unitary_decompositions.py b/src/qibo/transpiler/unitary_decompositions.py index e737ba649e..472347b0d4 100644 --- a/src/qibo/transpiler/unitary_decompositions.py +++ b/src/qibo/transpiler/unitary_decompositions.py @@ -1,7 +1,7 @@ import numpy as np from qibo import gates, matrices -from qibo.backends import GlobalBackend, NumpyBackend +from qibo.backends import NumpyBackend, _check_backend from qibo.config import raise_error magic_basis = np.array( @@ -50,8 +50,7 @@ def calculate_psi(unitary, magic_basis=magic_basis, backend=None): Returns: (ndarray) Eigenvectors in the computational basis and eigenvalues of :math:`U^{T} U`. """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) if backend.__class__.__name__ in [ "CupyBackend", @@ -154,9 +153,8 @@ def calculate_diagonal(unitary, ua, ub, va, vb): def magic_decomposition(unitary, backend=None): - if backend is None: # pragma: no cover - backend = GlobalBackend() """Decomposes an arbitrary unitary to (A1) from arXiv:quant-ph/0011050.""" + backend = _check_backend(backend) psi, eigvals = calculate_psi(unitary, backend=backend) psi_tilde = np.conj(np.sqrt(eigvals)) * np.dot(unitary, psi) va, vb = calculate_single_qubit_unitaries(psi) @@ -167,8 +165,7 @@ def magic_decomposition(unitary, backend=None): def to_bell_diagonal(ud, bell_basis=bell_basis, backend=None): """Transforms a matrix to the Bell basis and checks if it is diagonal.""" - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) ud = backend.cast(ud) bell_basis = backend.cast(bell_basis, dtype=bell_basis.dtype) @@ -248,8 +245,7 @@ def two_qubit_decomposition(q0, q1, unitary, backend=None): Returns: (list): gates implementing decomposition (24) from arXiv:quant-ph/0307177 """ - if backend is None: # pragma: no cover - backend = GlobalBackend() + backend = _check_backend(backend) ud_diag = to_bell_diagonal(unitary, backend=backend) ud = None diff --git a/src/qibo/transpiler/unroller.py b/src/qibo/transpiler/unroller.py index c62ae4e273..6b8f3e9c60 100644 --- a/src/qibo/transpiler/unroller.py +++ b/src/qibo/transpiler/unroller.py @@ -85,7 +85,7 @@ def __call__(self, circuit: Circuit): Returns: (:class:`qibo.models.circuit.Circuit`): equivalent circuit with native gates. """ - translated_circuit = circuit.__class__(circuit.nqubits) + translated_circuit = Circuit(**circuit.init_kwargs) for gate in circuit.queue: translated_circuit.add( translate_gate( diff --git a/tests/test_gates_gates.py b/tests/test_gates_gates.py index e014031eac..caabc73e57 100644 --- a/tests/test_gates_gates.py +++ b/tests/test_gates_gates.py @@ -1546,6 +1546,8 @@ def test_controlled_unitary_matrix(backend): ("GIVENS", (0, 1, 0.1)), ("RBS", (0, 1, 0.2)), ("ECR", (0, 1)), + ("SiSWAP", (0, 1)), + ("SiSWAPDG", (0, 1)), ] diff --git a/tests/test_global_backend.py b/tests/test_global_backend.py index 27386ed812..9ad3a4b2f4 100644 --- a/tests/test_global_backend.py +++ b/tests/test_global_backend.py @@ -91,3 +91,20 @@ def test_gate_matrix(): qibo.set_backend("numpy") gate = qibo.gates.H(0) matrix = gate.matrix + + +def test_check_backend(backend): + # testing when backend is not None + test = qibo.backends._check_backend(backend) + + assert test.name == backend.name + assert test.__class__ == backend.__class__ + + # testing when backend is None + test = None + test = qibo.backends._check_backend(test) + + target = qibo.backends.GlobalBackend() + + assert test.name == target.name + assert test.__class__ == target.__class__ diff --git a/tests/test_models_encodings.py b/tests/test_models_encodings.py index b71faa5c97..63cae6836b 100644 --- a/tests/test_models_encodings.py +++ b/tests/test_models_encodings.py @@ -1,6 +1,7 @@ """Tests for qibo.models.encodings""" import math +from itertools import product import numpy as np import pytest @@ -8,6 +9,7 @@ from qibo.models.encodings import ( comp_basis_encoder, + phase_encoder, unary_encoder, unary_encoder_random_gaussian, ) @@ -48,9 +50,58 @@ def test_comp_basis_encoder(backend, basis_element): backend.assert_allclose(state, target) +@pytest.mark.parametrize("kind", [None, list]) +@pytest.mark.parametrize("rotation", ["RX", "RY", "RZ"]) +def test_phase_encoder(backend, rotation, kind): + sampler = np.random.default_rng(1) + + nqubits = 3 + dims = 2**nqubits + + with pytest.raises(TypeError): + data = sampler.random((nqubits, nqubits)) + data = backend.cast(data, dtype=data.dtype) + phase_encoder(data, rotation=rotation) + with pytest.raises(TypeError): + data = sampler.random(nqubits) + data = backend.cast(data, dtype=data.dtype) + phase_encoder(data, rotation=True) + with pytest.raises(ValueError): + data = sampler.random(nqubits) + data = backend.cast(data, dtype=data.dtype) + phase_encoder(data, rotation="rzz") + + phases = np.random.rand(nqubits) + + if rotation in ["RX", "RY"]: + functions = list(product([np.cos, np.sin], repeat=nqubits)) + target = [] + for row in functions: + elem = 1.0 + for phase, func in zip(phases, row): + elem *= func(phase / 2) + if rotation == "RX" and func.__name__ == "sin": + elem *= -1.0j + target.append(elem) + else: + target = [np.exp(-0.5j * sum(phases))] + [0.0] * (dims - 1) + + target = np.array(target, dtype=complex) + target = backend.cast(target, dtype=target.dtype) + + if kind is not None: + phases = kind(phases) + + state = phase_encoder(phases, rotation=rotation) + state = backend.execute_circuit(state).state() + + backend.assert_allclose(state, target) + + +@pytest.mark.parametrize("kind", [None, list]) @pytest.mark.parametrize("architecture", ["tree", "diagonal"]) @pytest.mark.parametrize("nqubits", [8]) -def test_unary_encoder(backend, nqubits, architecture): +def test_unary_encoder(backend, nqubits, architecture, kind): sampler = np.random.default_rng(1) with pytest.raises(TypeError): @@ -76,6 +127,9 @@ def test_unary_encoder(backend, nqubits, architecture): data = 2 * sampler.random(nqubits) - 1 data = backend.cast(data, dtype=data.dtype) + if kind is not None: + data = kind(data) + circuit = unary_encoder(data, architecture=architecture) state = backend.execute_circuit(circuit).state() indexes = np.flatnonzero(state) diff --git a/tests/test_models_qcnn.py b/tests/test_models_qcnn.py index 581641035c..ab8d98e2b3 100644 --- a/tests/test_models_qcnn.py +++ b/tests/test_models_qcnn.py @@ -1,9 +1,10 @@ import math import numpy as np -import pytest -from qibo import Circuit, gates +import qibo +from qibo import gates +from qibo.models import Circuit from qibo.models.qcnn import QuantumCNN num_angles = 21 @@ -291,13 +292,57 @@ def test_qcnn_training(): test_qcnn = QuantumCNN(nqubits=4, nlayers=1, nclasses=2, params=init_theta) testcircuit = test_qcnn._circuit result = test_qcnn.minimize( - init_theta, data=data, labels=labels, nshots=1000, method="Powell" + init_theta, data=data, labels=labels, nshots=10000, method="Powell" ) # test Predictions function predictions = [] for n in range(len(data)): - predictions.append(test_qcnn.predict(data[n], nshots=1000)[0]) + predictions.append(test_qcnn.predict(data[n], nshots=10000)[0]) + + # test Accuracy function + predictions.append(1) + labels = np.array([[1], [-1], [1]]) + test_qcnn.Accuracy(labels, predictions) + + +def test_two_qubit_ansatz(): + c = Circuit(2) + c.add(gates.H(0)) + c.add(gates.RX(0, 0)) + c.add(gates.CNOT(1, 0)) + test_qcnn = QuantumCNN(4, 2, 2, twoqubitansatz=c) + + +def test_two_qubit_ansatz_training(): + # test qibojit case (copy initial state as quick-fix for in-place update) + qibo.set_backend("qibojit") + + c = Circuit(2) + c.add(gates.H(0)) + c.add(gates.RX(0, 0)) + c.add(gates.CNOT(1, 0)) + test_qcnn = QuantumCNN(4, 2, 2, twoqubitansatz=c) + + data = np.zeros([2, 16]) + for i in range(2): + data_i = np.random.rand(16) + data[i] = data_i / np.linalg.norm(data_i) + labels = [[1], [-1]] + + totalNParams = test_qcnn.nparams_layer * 2 + init_theta = [ + 0 for i in range(totalNParams + 1) + ] # totalNParams+1 to account for bias parameter. + + result = test_qcnn.minimize( + init_theta, data=data, labels=labels, nshots=10000, method="Powell" + ) + + # test Predictions function + predictions = [] + for n in range(len(data)): + predictions.append(test_qcnn.predict(data[n], nshots=10000)[0]) # test Accuracy function predictions.append(1) diff --git a/tests/test_quantum_info_clifford.py b/tests/test_quantum_info_clifford.py index 3290aefc31..ebde9aff5c 100644 --- a/tests/test_quantum_info_clifford.py +++ b/tests/test_quantum_info_clifford.py @@ -62,13 +62,14 @@ def test_clifford_from_circuit(backend, measurement): backend.assert_allclose(obj.probabilities(), result.probabilities()) +@pytest.mark.parametrize("seed", [1, 10]) @pytest.mark.parametrize("algorithm", ["AG04", "BM20"]) @pytest.mark.parametrize("nqubits", [1, 2, 3, 10, 50]) -def test_clifford_to_circuit(backend, nqubits, algorithm): +def test_clifford_to_circuit(backend, nqubits, algorithm, seed): if backend.__class__.__name__ == "TensorflowBackend": pytest.skip("CliffordBackend not defined for Tensorflow engine.") - clifford = random_clifford(nqubits, backend=backend) + clifford = random_clifford(nqubits, seed=seed, backend=backend) symplectic_matrix_original = Clifford.from_circuit( clifford, engine=backend diff --git a/tests/test_quantum_info_entanglement.py b/tests/test_quantum_info_entanglement.py new file mode 100644 index 0000000000..a98cf1909b --- /dev/null +++ b/tests/test_quantum_info_entanglement.py @@ -0,0 +1,175 @@ +import numpy as np +import pytest + +from qibo import Circuit, gates +from qibo.config import PRECISION_TOL +from qibo.quantum_info.entanglement import ( + concurrence, + entanglement_fidelity, + entanglement_of_formation, + entangling_capability, + meyer_wallach_entanglement, +) +from qibo.quantum_info.random_ensembles import random_density_matrix, random_statevector + + +@pytest.mark.parametrize("check_purity", [True, False]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("bipartition", [[0], [1]]) +def test_concurrence_and_formation(backend, bipartition, base, check_purity): + with pytest.raises(TypeError): + state = np.random.rand(2, 3) + state = backend.cast(state, dtype=state.dtype) + test = concurrence( + state, bipartition=bipartition, check_purity=check_purity, backend=backend + ) + with pytest.raises(TypeError): + state = random_statevector(4, backend=backend) + test = concurrence( + state, bipartition=bipartition, check_purity="True", backend=backend + ) + + if check_purity is True: + with pytest.raises(NotImplementedError): + state = backend.identity_density_matrix(2, normalize=False) + test = concurrence(state, bipartition=bipartition, backend=backend) + + nqubits = 2 + dim = 2**nqubits + state = random_statevector(dim, backend=backend) + concur = concurrence( + state, bipartition=bipartition, check_purity=check_purity, backend=backend + ) + ent_form = entanglement_of_formation( + state, + bipartition=bipartition, + base=base, + check_purity=check_purity, + backend=backend, + ) + backend.assert_allclose(0.0 <= concur <= np.sqrt(2), True) + backend.assert_allclose(0.0 <= ent_form <= 1.0, True) + + state = np.kron( + random_density_matrix(2, pure=True, backend=backend), + random_density_matrix(2, pure=True, backend=backend), + ) + concur = concurrence(state, bipartition, check_purity=check_purity, backend=backend) + ent_form = entanglement_of_formation( + state, + bipartition=bipartition, + base=base, + check_purity=check_purity, + backend=backend, + ) + backend.assert_allclose(concur, 0.0, atol=10 * PRECISION_TOL) + backend.assert_allclose(ent_form, 0.0, atol=PRECISION_TOL) + + +@pytest.mark.parametrize("check_hermitian", [False, True]) +@pytest.mark.parametrize("nqubits", [4, 6]) +@pytest.mark.parametrize("channel", [gates.DepolarizingChannel]) +def test_entanglement_fidelity(backend, channel, nqubits, check_hermitian): + with pytest.raises(TypeError): + test = entanglement_fidelity( + channel, nqubits=[0], check_hermitian=check_hermitian, backend=backend + ) + with pytest.raises(ValueError): + test = entanglement_fidelity( + channel, nqubits=0, check_hermitian=check_hermitian, backend=backend + ) + with pytest.raises(TypeError): + state = np.random.rand(2, 3, 2) + state = backend.cast(state, dtype=state.dtype) + test = entanglement_fidelity( + channel, + nqubits, + state=state, + check_hermitian=check_hermitian, + backend=backend, + ) + with pytest.raises(TypeError): + state = random_statevector(2, backend=backend) + test = entanglement_fidelity( + channel, nqubits, state=state, check_hermitian="False", backend=backend + ) + + channel = channel([0, 1], 0.5) + + # test on maximally entangled state + ent_fid = entanglement_fidelity( + channel, nqubits=nqubits, check_hermitian=check_hermitian, backend=backend + ) + backend.assert_allclose(ent_fid, 0.625, atol=PRECISION_TOL) + + # test with a state vector + state = backend.plus_state(nqubits) + ent_fid = entanglement_fidelity( + channel, + nqubits=nqubits, + state=state, + check_hermitian=check_hermitian, + backend=backend, + ) + backend.assert_allclose(ent_fid, 0.625, atol=PRECISION_TOL) + + # test on maximally mixed state + state = backend.identity_density_matrix(nqubits) + ent_fid = entanglement_fidelity( + channel, + nqubits=nqubits, + state=state, + check_hermitian=check_hermitian, + backend=backend, + ) + backend.assert_allclose(ent_fid, 1.0, atol=PRECISION_TOL) + + +def test_meyer_wallach_entanglement(backend): + nqubits = 2 + + circuit1 = Circuit(nqubits) + circuit1.add([gates.RX(0, np.pi / 4)] for _ in range(nqubits)) + + circuit2 = Circuit(nqubits) + circuit2.add([gates.RX(0, np.pi / 4)] for _ in range(nqubits)) + circuit2.add(gates.CNOT(0, 1)) + + backend.assert_allclose( + meyer_wallach_entanglement(circuit1, backend=backend), 0.0, atol=PRECISION_TOL + ) + + backend.assert_allclose( + meyer_wallach_entanglement(circuit2, backend=backend), 0.5, atol=PRECISION_TOL + ) + + +@pytest.mark.parametrize("seed", [None, 10, np.random.default_rng(10)]) +def test_entangling_capability(backend, seed): + with pytest.raises(TypeError): + circuit = Circuit(1) + samples = 0.5 + entangling_capability(circuit, samples, seed=seed, backend=backend) + with pytest.raises(TypeError): + circuit = Circuit(1) + entangling_capability(circuit, samples=10, seed="10", backend=backend) + + nqubits = 2 + samples = 100 + + c1 = Circuit(nqubits) + c1.add([gates.RX(q, 0, trainable=True) for q in range(nqubits)]) + c1.add(gates.CNOT(0, 1)) + c1.add([gates.RX(q, 0, trainable=True) for q in range(nqubits)]) + ent_mw1 = entangling_capability(c1, samples, seed=seed, backend=backend) + + c2 = Circuit(nqubits) + c2.add(gates.H(0)) + c2.add(gates.CNOT(0, 1)) + c2.add(gates.RX(0, 0, trainable=True)) + ent_mw2 = entangling_capability(c2, samples, seed=seed, backend=backend) + + c3 = Circuit(nqubits) + ent_mw3 = entangling_capability(c3, samples, seed=seed, backend=backend) + + backend.assert_allclose(ent_mw3 < ent_mw1 < ent_mw2, True) diff --git a/tests/test_quantum_info_entropies.py b/tests/test_quantum_info_entropies.py new file mode 100644 index 0000000000..3cf8b2d721 --- /dev/null +++ b/tests/test_quantum_info_entropies.py @@ -0,0 +1,736 @@ +import numpy as np +import pytest +from scipy.linalg import sqrtm + +from qibo.config import PRECISION_TOL +from qibo.quantum_info.entropies import ( + _matrix_power, + classical_relative_entropy, + classical_relative_renyi_entropy, + classical_renyi_entropy, + classical_tsallis_entropy, + entanglement_entropy, + relative_renyi_entropy, + relative_von_neumann_entropy, + renyi_entropy, + shannon_entropy, + tsallis_entropy, + von_neumann_entropy, +) +from qibo.quantum_info.random_ensembles import ( + random_density_matrix, + random_statevector, + random_unitary, +) + + +def test_shannon_entropy_errors(backend): + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = shannon_entropy(prob, -2, backend=backend) + with pytest.raises(TypeError): + prob = np.array([[1.0], [0.0]]) + prob = backend.cast(prob, dtype=prob.dtype) + test = shannon_entropy(prob, backend=backend) + with pytest.raises(TypeError): + prob = np.array([]) + prob = backend.cast(prob, dtype=prob.dtype) + test = shannon_entropy(prob, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, -1.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = shannon_entropy(prob, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.1, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = shannon_entropy(prob, backend=backend) + with pytest.raises(ValueError): + prob = np.array([0.5, 0.4999999]) + prob = backend.cast(prob, dtype=prob.dtype) + test = shannon_entropy(prob, backend=backend) + + +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +def test_shannon_entropy(backend, base): + prob_array = [1.0, 0.0] + result = shannon_entropy(prob_array, base, backend=backend) + backend.assert_allclose(result, 0.0) + + if base == 2: + prob_array = np.array([0.5, 0.5]) + prob_array = backend.cast(prob_array, dtype=prob_array.dtype) + result = shannon_entropy(prob_array, base, backend=backend) + backend.assert_allclose(result, 1.0) + + +@pytest.mark.parametrize("kind", [None, list]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +def test_classical_relative_entropy(backend, base, kind): + with pytest.raises(TypeError): + prob = np.random.rand(1, 2) + prob_q = np.random.rand(1, 5) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_entropy(prob, prob_q, backend=backend) + with pytest.raises(TypeError): + prob = np.random.rand(1, 2)[0] + prob_q = np.array([]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_entropy(prob, prob_q, backend=backend) + with pytest.raises(ValueError): + prob = np.array([-1, 2.0]) + prob_q = np.random.rand(1, 5)[0] + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_entropy(prob, prob_q, backend=backend) + with pytest.raises(ValueError): + prob = np.random.rand(1, 2)[0] + prob_q = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_entropy(prob, prob_q, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob_q = np.random.rand(1, 2)[0] + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_entropy(prob, prob_q, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob_q = np.array([0.0, 1.0]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_entropy(prob, prob_q, base=-2, backend=backend) + + prob_p = np.random.rand(10) + prob_q = np.random.rand(10) + prob_p /= np.sum(prob_p) + prob_q /= np.sum(prob_q) + + target = np.sum(prob_p * np.log(prob_p) / np.log(base)) - np.sum( + prob_p * np.log(prob_q) / np.log(base) + ) + + if kind is not None: + prob_p, prob_q = kind(prob_p), kind(prob_q) + + divergence = classical_relative_entropy(prob_p, prob_q, base=base, backend=backend) + + backend.assert_allclose(divergence, target, atol=1e-5) + + +@pytest.mark.parametrize("kind", [None, list]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("alpha", [0, 1, 2, 3, np.inf]) +def test_classical_renyi_entropy(backend, alpha, base, kind): + with pytest.raises(TypeError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha="2", backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha=-2, backend=backend) + with pytest.raises(TypeError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha, base="2", backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha, base=-2, backend=backend) + with pytest.raises(TypeError): + prob = np.array([[1.0], [0.0]]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha, backend=backend) + with pytest.raises(TypeError): + prob = np.array([]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, -1.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.1, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha, backend=backend) + with pytest.raises(ValueError): + prob = np.array([0.5, 0.4999999]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_renyi_entropy(prob, alpha, backend=backend) + + prob_dist = np.random.rand(10) + prob_dist /= np.sum(prob_dist) + + if alpha == 0.0: + target = np.log2(len(prob_dist)) / np.log2(base) + elif alpha == 1: + target = shannon_entropy(prob_dist, base=base, backend=backend) + elif alpha == 2: + target = -1 * np.log2(np.sum(prob_dist**2)) / np.log2(base) + elif alpha == np.inf: + target = -1 * np.log2(max(prob_dist)) / np.log2(base) + else: + target = (1 / (1 - alpha)) * np.log2(np.sum(prob_dist**alpha)) / np.log2(base) + + if kind is not None: + prob_dist = kind(prob_dist) + + renyi_ent = classical_renyi_entropy(prob_dist, alpha, base=base, backend=backend) + + backend.assert_allclose(renyi_ent, target, atol=1e-5) + + +@pytest.mark.parametrize("kind", [None, list]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("alpha", [0, 1 / 2, 1, 2, 3, np.inf]) +def test_classical_relative_renyi_entropy(backend, alpha, base, kind): + with pytest.raises(TypeError): + prob = np.random.rand(1, 2) + prob_q = np.random.rand(1, 5) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha, base, backend=backend + ) + with pytest.raises(TypeError): + prob = np.random.rand(1, 2)[0] + prob_q = np.array([]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha, base, backend=backend + ) + with pytest.raises(ValueError): + prob = np.array([-1, 2.0]) + prob_q = np.random.rand(1, 5)[0] + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha, base, backend=backend + ) + with pytest.raises(ValueError): + prob = np.random.rand(1, 2)[0] + prob_q = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha, base, backend=backend + ) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob_q = np.random.rand(1, 2)[0] + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha, base, backend=backend + ) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob_q = np.array([0.0, 1.0]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha, base=-2, backend=backend + ) + with pytest.raises(TypeError): + prob = np.array([1.0, 0.0]) + prob_q = np.array([0.0, 1.0]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha="1", base=base, backend=backend + ) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob_q = np.array([0.0, 1.0]) + prob = backend.cast(prob, dtype=prob.dtype) + prob_q = backend.cast(prob_q, dtype=prob_q.dtype) + test = classical_relative_renyi_entropy( + prob, prob_q, alpha=-2, base=base, backend=backend + ) + + prob_p = np.random.rand(10) + prob_q = np.random.rand(10) + prob_p /= np.sum(prob_p) + prob_q /= np.sum(prob_q) + + if alpha == 0.5: + target = -2 * np.log2(np.sum(np.sqrt(prob_p * prob_q))) / np.log2(base) + elif alpha == 1.0: + target = classical_relative_entropy(prob_p, prob_q, base=base, backend=backend) + elif alpha == np.inf: + target = np.log2(max(prob_p / prob_q)) / np.log2(base) + else: + target = ( + (1 / (alpha - 1)) + * np.log2(np.sum(prob_p**alpha * prob_q ** (1 - alpha))) + / np.log2(base) + ) + + if kind is not None: + prob_p, prob_q = kind(prob_p), kind(prob_q) + + divergence = classical_relative_renyi_entropy( + prob_p, prob_q, alpha=alpha, base=base, backend=backend + ) + + backend.assert_allclose(divergence, target, atol=1e-5) + + +@pytest.mark.parametrize("kind", [None, list]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("alpha", [0, 1, 2, 3]) +def test_classical_tsallis_entropy(backend, alpha, base, kind): + with pytest.raises(TypeError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha="2", backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha=-2, backend=backend) + with pytest.raises(TypeError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha, base="2", backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha, base=-2, backend=backend) + with pytest.raises(TypeError): + prob = np.array([[1.0], [0.0]]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha, backend=backend) + with pytest.raises(TypeError): + prob = np.array([]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.0, -1.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha, backend=backend) + with pytest.raises(ValueError): + prob = np.array([1.1, 0.0]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha, backend=backend) + with pytest.raises(ValueError): + prob = np.array([0.5, 0.4999999]) + prob = backend.cast(prob, dtype=prob.dtype) + test = classical_tsallis_entropy(prob, alpha, backend=backend) + + prob_dist = np.random.rand(10) + prob_dist /= np.sum(prob_dist) + + if alpha == 1.0: + target = shannon_entropy(prob_dist, base=base, backend=backend) + else: + target = (1 / (1 - alpha)) * (np.sum(prob_dist**alpha) - 1) + + if kind is not None: + prob_dist = kind(prob_dist) + + backend.assert_allclose( + classical_tsallis_entropy(prob_dist, alpha=alpha, base=base, backend=backend), + target, + atol=1e-5, + ) + + +@pytest.mark.parametrize("check_hermitian", [False, True]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +def test_von_neumann_entropy(backend, base, check_hermitian): + with pytest.raises(TypeError): + state = np.random.rand(2, 3) + state = backend.cast(state, dtype=state.dtype) + test = von_neumann_entropy( + state, base=base, check_hermitian=check_hermitian, backend=backend + ) + with pytest.raises(ValueError): + state = np.array([1.0, 0.0]) + state = backend.cast(state, dtype=state.dtype) + test = von_neumann_entropy( + state, base=0, check_hermitian=check_hermitian, backend=backend + ) + with pytest.raises(TypeError): + state = np.array([1.0, 0.0]) + state = backend.cast(state, dtype=state.dtype) + test = von_neumann_entropy( + state, base=base, check_hermitian="False", backend=backend + ) + + if backend.__class__.__name__ in ["CupyBackend", "CuQuantumBackend"]: + with pytest.raises(NotImplementedError): + state = random_unitary(4, backend=backend) + test = von_neumann_entropy( + state, base=base, check_hermitian=True, backend=backend + ) + else: + state = random_unitary(4, backend=backend) + test = von_neumann_entropy( + state, base=base, check_hermitian=True, backend=backend + ) + + state = np.array([1.0, 0.0]) + state = backend.cast(state, dtype=state.dtype) + backend.assert_allclose( + von_neumann_entropy(state, backend=backend), 0.0, atol=PRECISION_TOL + ) + + state = np.array([1.0, 0.0, 0.0, 0.0]) + state = np.outer(state, state) + state = backend.cast(state, dtype=state.dtype) + + nqubits = 2 + state = backend.identity_density_matrix(nqubits) + if base == 2: + test = 2.0 + elif base == 10: + test = 0.6020599913279624 + elif base == np.e: + test = 1.3862943611198906 + else: + test = 0.8613531161467861 + + backend.assert_allclose( + von_neumann_entropy( + state, base, check_hermitian=check_hermitian, backend=backend + ), + test, + ) + + +@pytest.mark.parametrize("check_hermitian", [False, True]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +def test_relative_entropy(backend, base, check_hermitian): + with pytest.raises(TypeError): + state = np.random.rand(2, 3) + state = backend.cast(state, dtype=state.dtype) + target = random_density_matrix(2, pure=True, backend=backend) + test = relative_von_neumann_entropy( + state, target, base=base, check_hermitian=check_hermitian, backend=backend + ) + with pytest.raises(TypeError): + target = np.random.rand(2, 3) + target = backend.cast(target, dtype=target.dtype) + state = random_density_matrix(2, pure=True, backend=backend) + test = relative_von_neumann_entropy( + state, target, base=base, check_hermitian=check_hermitian, backend=backend + ) + with pytest.raises(ValueError): + state = np.array([1.0, 0.0]) + state = backend.cast(state, dtype=state.dtype) + target = np.array([0.0, 1.0]) + target = backend.cast(target, dtype=target.dtype) + test = relative_von_neumann_entropy( + state, target, base=0, check_hermitian=check_hermitian, backend=backend + ) + with pytest.raises(TypeError): + state = np.array([1.0, 0.0]) + state = backend.cast(state, dtype=state.dtype) + target = np.array([0.0, 1.0]) + target = backend.cast(target, dtype=target.dtype) + test = relative_von_neumann_entropy( + state, target, base=base, check_hermitian="False", backend=backend + ) + + nqubits = 2 + dims = 2**nqubits + + state = random_density_matrix(dims, backend=backend) + target = backend.identity_density_matrix(nqubits, normalize=True) + + backend.assert_allclose( + relative_von_neumann_entropy(state, target, base, check_hermitian, backend), + np.log(dims) / np.log(base) + - von_neumann_entropy( + state, base=base, check_hermitian=check_hermitian, backend=backend + ), + atol=1e-5, + ) + + state = backend.cast([1.0, 0.0], dtype=np.float64) + target = backend.cast([0.0, 1.0], dtype=np.float64) + + assert relative_von_neumann_entropy(state, target, backend=backend) == 0.0 + + # for coverage when GPUs are present + if backend.__class__.__name__ in ["CupyBackend", "CuQuantumBackend"]: + with pytest.raises(NotImplementedError): + state = random_unitary(4, backend=backend) + target = random_density_matrix(4, backend=backend) + test = relative_von_neumann_entropy( + state, target, base=base, check_hermitian=True, backend=backend + ) + with pytest.raises(NotImplementedError): + target = random_unitary(4, backend=backend) + state = random_density_matrix(4, backend=backend) + test = relative_von_neumann_entropy( + state, target, base=base, check_hermitian=True, backend=backend + ) + else: + state = random_unitary(4, backend=backend) + target = random_unitary(4, backend=backend) + test = relative_von_neumann_entropy( + state, target, base=base, check_hermitian=True, backend=backend + ) + + +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("alpha", [0, 1, 2, 3, np.inf]) +def test_renyi_entropy(backend, alpha, base): + with pytest.raises(TypeError): + state = np.random.rand(2, 3) + state = backend.cast(state, dtype=state.dtype) + test = renyi_entropy(state, alpha=alpha, base=base, backend=backend) + with pytest.raises(TypeError): + state = random_statevector(4, backend=backend) + test = renyi_entropy(state, alpha="2", base=base, backend=backend) + with pytest.raises(ValueError): + state = random_statevector(4, backend=backend) + test = renyi_entropy(state, alpha=-1, base=base, backend=backend) + with pytest.raises(ValueError): + state = random_statevector(4, backend=backend) + test = renyi_entropy(state, alpha=alpha, base=0, backend=backend) + + state = random_density_matrix(4, backend=backend) + + if alpha == 0.0: + target = np.log2(len(state)) / np.log2(base) + elif alpha == 1.0: + target = von_neumann_entropy(state, base=base, backend=backend) + elif alpha == np.inf: + target = backend.calculate_norm_density_matrix(state, order=2) + target = -1 * np.log2(target) / np.log2(base) + else: + target = np.log2(np.trace(np.linalg.matrix_power(state, alpha))) + target = (1 / (1 - alpha)) * target / np.log2(base) + + backend.assert_allclose( + renyi_entropy(state, alpha=alpha, base=base, backend=backend), target, atol=1e-5 + ) + + # test pure state + state = random_density_matrix(4, pure=True, backend=backend) + backend.assert_allclose( + renyi_entropy(state, alpha=alpha, base=base, backend=backend), 0.0, atol=1e-8 + ) + + +@pytest.mark.parametrize( + ["state_flag", "target_flag"], [[True, True], [False, True], [True, False]] +) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("alpha", [0, 1, 2, 3, 5.4, np.inf]) +def test_relative_renyi_entropy(backend, alpha, base, state_flag, target_flag): + with pytest.raises(TypeError): + state = np.random.rand(2, 3) + state = backend.cast(state, dtype=state.dtype) + target = random_density_matrix(4, backend=backend) + test = relative_renyi_entropy( + state, target, alpha=alpha, base=base, backend=backend + ) + with pytest.raises(TypeError): + target = np.random.rand(2, 3) + target = backend.cast(target, dtype=target.dtype) + state = random_density_matrix(4, backend=backend) + test = relative_renyi_entropy( + state, target, alpha=alpha, base=base, backend=backend + ) + with pytest.raises(TypeError): + state = random_statevector(4, backend=backend) + target = random_statevector(4, backend=backend) + test = relative_renyi_entropy( + state, target, alpha="2", base=base, backend=backend + ) + with pytest.raises(ValueError): + state = random_statevector(4, backend=backend) + target = random_statevector(4, backend=backend) + test = relative_renyi_entropy( + state, target, alpha=-1, base=base, backend=backend + ) + with pytest.raises(ValueError): + state = random_statevector(4, backend=backend) + target = random_statevector(4, backend=backend) + test = relative_renyi_entropy( + state, target, alpha=alpha, base=0, backend=backend + ) + + state = ( + random_statevector(4, backend=backend) + if state_flag + else random_density_matrix(4, backend=backend) + ) + target = ( + random_statevector(4, backend=backend) + if target_flag + else random_density_matrix(4, backend=backend) + ) + + if state_flag and target_flag: + backend.assert_allclose( + relative_renyi_entropy(state, target, alpha, base, backend), 0.0, atol=1e-5 + ) + else: + if target_flag and alpha > 1: + with pytest.raises(NotImplementedError): + relative_renyi_entropy(state, target, alpha, base, backend) + else: + if alpha == 1.0: + log = relative_von_neumann_entropy(state, target, base, backend=backend) + elif alpha == np.inf: + new_state = _matrix_power(state, 0.5, backend) + new_target = _matrix_power(target, 0.5, backend) + + log = np.log2( + backend.calculate_norm_density_matrix( + new_state @ new_target, order=1 + ) + ) + + log = -2 * log / np.log2(base) + else: + if len(state.shape) == 1: + state = np.outer(state, np.conj(state)) + + if len(target.shape) == 1: + target = np.outer(target, np.conj(target)) + + log = _matrix_power(state, alpha, backend) + log = log @ _matrix_power(target, 1 - alpha, backend) + log = np.log2(np.trace(log)) + + log = (1 / (alpha - 1)) * log / np.log2(base) + + backend.assert_allclose( + relative_renyi_entropy( + state, target, alpha=alpha, base=base, backend=backend + ), + log, + atol=1e-5, + ) + + # test pure states + state = random_density_matrix(4, pure=True, backend=backend) + target = random_density_matrix(4, pure=True, backend=backend) + backend.assert_allclose( + relative_renyi_entropy(state, target, alpha=alpha, base=base, backend=backend), + 0.0, + atol=1e-8, + ) + + +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("alpha", [0, 1, 2, 3, 5.4]) +def test_tsallis_entropy(backend, alpha, base): + with pytest.raises(TypeError): + state = np.random.rand(2, 3) + state = backend.cast(state, dtype=state.dtype) + test = tsallis_entropy(state, alpha=alpha, base=base, backend=backend) + with pytest.raises(TypeError): + state = random_statevector(4, backend=backend) + test = tsallis_entropy(state, alpha="2", base=base, backend=backend) + with pytest.raises(ValueError): + state = random_statevector(4, backend=backend) + test = tsallis_entropy(state, alpha=-1, base=base, backend=backend) + with pytest.raises(ValueError): + state = random_statevector(4, backend=backend) + test = tsallis_entropy(state, alpha=alpha, base=0, backend=backend) + + state = random_density_matrix(4, backend=backend) + + if alpha == 1.0: + target = von_neumann_entropy(state, base=base, backend=backend) + else: + target = (1 / (1 - alpha)) * ( + np.trace(_matrix_power(state, alpha, backend)) - 1 + ) + + backend.assert_allclose( + tsallis_entropy(state, alpha=alpha, base=base, backend=backend), + target, + atol=1e-5, + ) + + # test pure state + state = random_density_matrix(4, pure=True, backend=backend) + backend.assert_allclose( + tsallis_entropy(state, alpha=alpha, base=base, backend=backend), 0.0, atol=1e-5 + ) + + +@pytest.mark.parametrize("check_hermitian", [False, True]) +@pytest.mark.parametrize("base", [2, 10, np.e, 5]) +@pytest.mark.parametrize("bipartition", [[0], [1]]) +def test_entanglement_entropy(backend, bipartition, base, check_hermitian): + with pytest.raises(TypeError): + state = np.random.rand(2, 3) + state = backend.cast(state, dtype=state.dtype) + test = entanglement_entropy( + state, + bipartition=bipartition, + base=base, + check_hermitian=check_hermitian, + backend=backend, + ) + with pytest.raises(ValueError): + state = np.array([1.0, 0.0]) + state = backend.cast(state, dtype=state.dtype) + test = entanglement_entropy( + state, + bipartition=bipartition, + base=0, + check_hermitian=check_hermitian, + backend=backend, + ) + if backend.__class__.__name__ == "CupyBackend": + with pytest.raises(NotImplementedError): + state = random_unitary(4, backend=backend) + test = entanglement_entropy( + state, + bipartition=bipartition, + base=base, + check_hermitian=True, + backend=backend, + ) + + # Bell state + state = np.array([1.0, 0.0, 0.0, 1.0]) / np.sqrt(2) + state = backend.cast(state, dtype=state.dtype) + + entang_entrop = entanglement_entropy( + state, + bipartition=bipartition, + base=base, + check_hermitian=check_hermitian, + backend=backend, + ) + + if base == 2: + test = 1.0 + elif base == 10: + test = 0.30102999566398125 + elif base == np.e: + test = 0.6931471805599454 + else: + test = 0.4306765580733931 + + backend.assert_allclose(entang_entrop, test, atol=PRECISION_TOL) + + # Product state + state = np.kron( + random_statevector(2, backend=backend), random_statevector(2, backend=backend) + ) + + entang_entrop = entanglement_entropy( + state, + bipartition=bipartition, + base=base, + check_hermitian=check_hermitian, + backend=backend, + ) + + backend.assert_allclose(entang_entrop, 0.0, atol=PRECISION_TOL) diff --git a/tests/test_quantum_info_metrics.py b/tests/test_quantum_info_metrics.py index 0e7b86f21b..793bb510a5 100644 --- a/tests/test_quantum_info_metrics.py +++ b/tests/test_quantum_info_metrics.py @@ -7,13 +7,7 @@ average_gate_fidelity, bures_angle, bures_distance, - concurrence, diamond_norm, - entanglement_entropy, - entanglement_fidelity, - entanglement_of_formation, - entangling_capability, - entropy, expressibility, fidelity, frame_potential, @@ -21,7 +15,6 @@ hilbert_schmidt_distance, impurity, infidelity, - meyer_wallach_entanglement, process_fidelity, process_infidelity, purity, @@ -30,7 +23,6 @@ from qibo.quantum_info.random_ensembles import ( random_density_matrix, random_hermitian, - random_statevector, random_unitary, ) from qibo.quantum_info.superoperator_transformations import to_choi @@ -59,183 +51,6 @@ def test_purity_and_impurity(backend): backend.assert_allclose(impurity(state), 1.0 - 1.0 / dim, atol=PRECISION_TOL) -@pytest.mark.parametrize("check_purity", [True, False]) -@pytest.mark.parametrize("base", [2, 10, np.e, 5]) -@pytest.mark.parametrize("bipartition", [[0], [1]]) -def test_concurrence_and_formation(backend, bipartition, base, check_purity): - with pytest.raises(TypeError): - state = np.random.rand(2, 3) - state = backend.cast(state, dtype=state.dtype) - test = concurrence( - state, bipartition=bipartition, check_purity=check_purity, backend=backend - ) - with pytest.raises(TypeError): - state = random_statevector(4, backend=backend) - test = concurrence( - state, bipartition=bipartition, check_purity="True", backend=backend - ) - - if check_purity is True: - with pytest.raises(NotImplementedError): - state = backend.identity_density_matrix(2, normalize=False) - test = concurrence(state, bipartition=bipartition, backend=backend) - - nqubits = 2 - dim = 2**nqubits - state = random_statevector(dim, backend=backend) - concur = concurrence( - state, bipartition=bipartition, check_purity=check_purity, backend=backend - ) - ent_form = entanglement_of_formation( - state, - bipartition=bipartition, - base=base, - check_purity=check_purity, - backend=backend, - ) - backend.assert_allclose(0.0 <= concur <= np.sqrt(2), True) - backend.assert_allclose(0.0 <= ent_form <= 1.0, True) - - state = np.kron( - random_density_matrix(2, pure=True, backend=backend), - random_density_matrix(2, pure=True, backend=backend), - ) - concur = concurrence(state, bipartition, check_purity=check_purity, backend=backend) - ent_form = entanglement_of_formation( - state, - bipartition=bipartition, - base=base, - check_purity=check_purity, - backend=backend, - ) - backend.assert_allclose(concur, 0.0, atol=10 * PRECISION_TOL) - backend.assert_allclose(ent_form, 0.0, atol=PRECISION_TOL) - - -@pytest.mark.parametrize("check_hermitian", [False, True]) -@pytest.mark.parametrize("base", [2, 10, np.e, 5]) -def test_entropy(backend, base, check_hermitian): - with pytest.raises(TypeError): - state = np.random.rand(2, 3) - state = backend.cast(state, dtype=state.dtype) - test = entropy( - state, base=base, check_hermitian=check_hermitian, backend=backend - ) - with pytest.raises(ValueError): - state = np.array([1.0, 0.0]) - state = backend.cast(state, dtype=state.dtype) - test = entropy(state, base=0, check_hermitian=check_hermitian, backend=backend) - with pytest.raises(TypeError): - state = np.array([1.0, 0.0]) - state = backend.cast(state, dtype=state.dtype) - test = entropy(state, base=base, check_hermitian="False", backend=backend) - - if backend.__class__.__name__ in ["CupyBackend", "CuQuantumBackend"]: - with pytest.raises(NotImplementedError): - state = random_unitary(4, backend=backend) - test = entropy(state, base=base, check_hermitian=True, backend=backend) - else: - state = random_unitary(4, backend=backend) - test = entropy(state, base=base, check_hermitian=True, backend=backend) - - state = np.array([1.0, 0.0]) - state = backend.cast(state, dtype=state.dtype) - backend.assert_allclose(entropy(state, backend=backend), 0.0, atol=PRECISION_TOL) - - state = np.array([1.0, 0.0, 0.0, 0.0]) - state = np.outer(state, state) - state = backend.cast(state, dtype=state.dtype) - - nqubits = 2 - state = backend.identity_density_matrix(nqubits) - if base == 2: - test = 2.0 - elif base == 10: - test = 0.6020599913279624 - elif base == np.e: - test = 1.3862943611198906 - else: - test = 0.8613531161467861 - - backend.assert_allclose( - entropy(state, base, check_hermitian=check_hermitian, backend=backend), test - ) - - -@pytest.mark.parametrize("check_hermitian", [False, True]) -@pytest.mark.parametrize("base", [2, 10, np.e, 5]) -@pytest.mark.parametrize("bipartition", [[0], [1]]) -def test_entanglement_entropy(backend, bipartition, base, check_hermitian): - with pytest.raises(TypeError): - state = np.random.rand(2, 3) - state = backend.cast(state, dtype=state.dtype) - test = entanglement_entropy( - state, - bipartition=bipartition, - base=base, - check_hermitian=check_hermitian, - backend=backend, - ) - with pytest.raises(ValueError): - state = np.array([1.0, 0.0]) - state = backend.cast(state, dtype=state.dtype) - test = entanglement_entropy( - state, - bipartition=bipartition, - base=0, - check_hermitian=check_hermitian, - backend=backend, - ) - if backend.__class__.__name__ == "CupyBackend": - with pytest.raises(NotImplementedError): - state = random_unitary(4, backend=backend) - test = entanglement_entropy( - state, - bipartition=bipartition, - base=base, - check_hermitian=True, - backend=backend, - ) - - # Bell state - state = np.array([1.0, 0.0, 0.0, 1.0]) / np.sqrt(2) - state = backend.cast(state, dtype=state.dtype) - - entang_entrop = entanglement_entropy( - state, - bipartition=bipartition, - base=base, - check_hermitian=check_hermitian, - backend=backend, - ) - - if base == 2: - test = 1.0 - elif base == 10: - test = 0.30102999566398125 - elif base == np.e: - test = 0.6931471805599454 - else: - test = 0.4306765580733931 - - backend.assert_allclose(entang_entrop, test, atol=PRECISION_TOL) - - # Product state - state = np.kron( - random_statevector(2, backend=backend), random_statevector(2, backend=backend) - ) - - entang_entrop = entanglement_entropy( - state, - bipartition=bipartition, - base=base, - check_hermitian=check_hermitian, - backend=backend, - ) - - backend.assert_allclose(entang_entrop, 0.0, atol=PRECISION_TOL) - - @pytest.mark.parametrize("check_hermitian", [False, True]) def test_trace_distance(backend, check_hermitian): with pytest.raises(TypeError): @@ -445,65 +260,6 @@ def test_fidelity_and_infidelity_and_bures(backend, check_hermitian): test = fidelity(state, target, check_hermitian=True, backend=backend) -@pytest.mark.parametrize("check_hermitian", [False, True]) -@pytest.mark.parametrize("nqubits", [4, 6]) -@pytest.mark.parametrize("channel", [gates.DepolarizingChannel]) -def test_entanglement_fidelity(backend, channel, nqubits, check_hermitian): - with pytest.raises(TypeError): - test = entanglement_fidelity( - channel, nqubits=[0], check_hermitian=check_hermitian, backend=backend - ) - with pytest.raises(ValueError): - test = entanglement_fidelity( - channel, nqubits=0, check_hermitian=check_hermitian, backend=backend - ) - with pytest.raises(TypeError): - state = np.random.rand(2, 3, 2) - state = backend.cast(state, dtype=state.dtype) - test = entanglement_fidelity( - channel, - nqubits, - state=state, - check_hermitian=check_hermitian, - backend=backend, - ) - with pytest.raises(TypeError): - state = random_statevector(2, backend=backend) - test = entanglement_fidelity( - channel, nqubits, state=state, check_hermitian="False", backend=backend - ) - - channel = channel([0, 1], 0.5) - - # test on maximally entangled state - ent_fid = entanglement_fidelity( - channel, nqubits=nqubits, check_hermitian=check_hermitian, backend=backend - ) - backend.assert_allclose(ent_fid, 0.625, atol=PRECISION_TOL) - - # test with a state vector - state = backend.plus_state(nqubits) - ent_fid = entanglement_fidelity( - channel, - nqubits=nqubits, - state=state, - check_hermitian=check_hermitian, - backend=backend, - ) - backend.assert_allclose(ent_fid, 0.625, atol=PRECISION_TOL) - - # test on maximally mixed state - state = backend.identity_density_matrix(nqubits) - ent_fid = entanglement_fidelity( - channel, - nqubits=nqubits, - state=state, - check_hermitian=check_hermitian, - backend=backend, - ) - backend.assert_allclose(ent_fid, 1.0, atol=PRECISION_TOL) - - def test_process_fidelity_and_infidelity(backend): d = 2 with pytest.raises(TypeError): @@ -572,56 +328,6 @@ def test_diamond_norm(backend, nqubits): backend.assert_allclose(dnorm, 0.0, atol=PRECISION_TOL) -def test_meyer_wallach_entanglement(backend): - nqubits = 2 - - circuit1 = Circuit(nqubits) - circuit1.add([gates.RX(0, np.pi / 4)] for _ in range(nqubits)) - - circuit2 = Circuit(nqubits) - circuit2.add([gates.RX(0, np.pi / 4)] for _ in range(nqubits)) - circuit2.add(gates.CNOT(0, 1)) - - backend.assert_allclose( - meyer_wallach_entanglement(circuit1, backend=backend), 0.0, atol=PRECISION_TOL - ) - - backend.assert_allclose( - meyer_wallach_entanglement(circuit2, backend=backend), 0.5, atol=PRECISION_TOL - ) - - -@pytest.mark.parametrize("seed", [None, 10, np.random.default_rng(10)]) -def test_entangling_capability(backend, seed): - with pytest.raises(TypeError): - circuit = Circuit(1) - samples = 0.5 - entangling_capability(circuit, samples, seed=seed, backend=backend) - with pytest.raises(TypeError): - circuit = Circuit(1) - entangling_capability(circuit, samples=10, seed="10", backend=backend) - - nqubits = 2 - samples = 100 - - c1 = Circuit(nqubits) - c1.add([gates.RX(q, 0, trainable=True) for q in range(nqubits)]) - c1.add(gates.CNOT(0, 1)) - c1.add([gates.RX(q, 0, trainable=True) for q in range(nqubits)]) - ent_mw1 = entangling_capability(c1, samples, seed=seed, backend=backend) - - c2 = Circuit(nqubits) - c2.add(gates.H(0)) - c2.add(gates.CNOT(0, 1)) - c2.add(gates.RX(0, 0, trainable=True)) - ent_mw2 = entangling_capability(c2, samples, seed=seed, backend=backend) - - c3 = Circuit(nqubits) - ent_mw3 = entangling_capability(c3, samples, seed=seed, backend=backend) - - backend.assert_allclose(ent_mw3 < ent_mw1 < ent_mw2, True) - - def test_expressibility(backend): with pytest.raises(TypeError): circuit = Circuit(1) diff --git a/tests/test_quantum_info_quantum_networks.py b/tests/test_quantum_info_quantum_networks.py index a84103ece5..1f20a97b45 100644 --- a/tests/test_quantum_info_quantum_networks.py +++ b/tests/test_quantum_info_quantum_networks.py @@ -28,6 +28,13 @@ def test_errors(backend): matrix = random_density_matrix(2**3, backend=backend) net = QuantumNetwork(matrix, (2,) * 3, backend=backend) + comb_partition = (2,) * 4 + comb_sys_out = (False, True) * 2 + comb = random_density_matrix(2**4, backend=backend) + comb_choi = QuantumNetwork( + comb, comb_partition, system_output=comb_sys_out, backend=backend + ) + with pytest.raises(TypeError): QuantumNetwork(channel.to_choi(backend=backend), partition=True) @@ -46,13 +53,13 @@ def test_errors(backend): QuantumNetwork(channel.to_choi(backend=backend), partition=(1, 2), pure="True") with pytest.raises(ValueError): - network.hermitian(precision_tol=-1e-8) + network.is_hermitian(precision_tol=-1e-8) with pytest.raises(ValueError): - network.unital(precision_tol=-1e-8) + network.is_unital(precision_tol=-1e-8) with pytest.raises(ValueError): - network.causal(precision_tol=-1e-8) + network.is_causal(precision_tol=-1e-8) with pytest.raises(TypeError): network + 1 @@ -88,9 +95,21 @@ def test_errors(backend): with pytest.raises(NotImplementedError): net @ net - with pytest.raises(ValueError): + with pytest.raises(NotImplementedError): net @ network + with pytest.raises(ValueError): + network @ net + + with pytest.raises(ValueError): + network @ QuantumNetwork(comb, (16, 16), pure=True, backend=backend) + + with pytest.raises(ValueError): + comb_choi @ QuantumNetwork(comb, (16, 16), pure=True, backend=backend) + + with pytest.raises(ValueError): + comb_choi @ net + with pytest.raises(ValueError): QuantumNetwork(matrix, (1, 2), backend=backend) @@ -155,11 +174,11 @@ def test_parameters(backend): backend.assert_allclose(network.partition, partition) backend.assert_allclose(network.system_output, (False, True)) - assert network.causal() - assert network.unital() - assert network.hermitian() - assert network.positive_semidefinite() - assert network.channel() + assert network.is_causal() + assert network.is_unital() + assert network.is_hermitian() + assert network.is_positive_semidefinite() + assert network.is_channel() def test_with_states(backend): @@ -186,8 +205,8 @@ def test_with_states(backend): state_output_link.matrix(backend=backend).reshape((dims, dims)), state_output ) - assert network_state.hermitian() - assert network_state.positive_semidefinite() + assert network_state.is_hermitian() + assert network_state.is_positive_semidefinite() @pytest.mark.parametrize("subscript", ["jk,kl->jl", "jk,lj->lk"]) @@ -223,25 +242,26 @@ def test_with_unitaries(backend, subscript): def test_with_comb(backend): - subscript = "jklm,lmno->jkno" - partition = (2,) * 4 - sys_out = (False, True) * 2 + subscript = "jklm,kl->jm" + comb_partition = (2,) * 4 + channel_partition = (2,) * 2 + comb_sys_out = (False, True) * 2 + channel_sys_out = (False, True) comb = random_density_matrix(2**4, backend=backend) - comb_2 = random_density_matrix(2**4, backend=backend) + channel = random_density_matrix(2**2, backend=backend) - comb_choi = QuantumNetwork(comb, partition, system_output=sys_out, backend=backend) - comb_choi_2 = QuantumNetwork( - comb_2, partition, system_output=sys_out, backend=backend + comb_choi = QuantumNetwork( + comb, comb_partition, system_output=comb_sys_out, backend=backend + ) + channel_choi = QuantumNetwork( + channel, channel_partition, system_output=channel_sys_out, backend=backend ) - comb_choi_3 = QuantumNetwork( - comb @ comb_2, partition, system_output=sys_out, backend=backend - ).to_full(backend) - test = comb_choi.link_product(comb_choi_2, subscript).to_full(backend) + test = comb_choi.link_product(channel_choi, subscript).to_full(backend) + channel_choi2 = comb_choi @ channel_choi - backend.assert_allclose(test, comb_choi_3, atol=1e-5) - backend.assert_allclose(test, (comb_choi @ comb_choi_2).to_full(backend), atol=1e-5) + backend.assert_allclose(test, channel_choi2.to_full(backend), atol=1e-5) def test_apply(backend): @@ -265,9 +285,9 @@ def test_non_hermitian_and_prints(backend): matrix = random_gaussian_matrix(dims**2, backend=backend) network = QuantumNetwork(matrix, (dims, dims), pure=False, backend=backend) - assert not network.hermitian() - assert not network.causal() - assert not network.positive_semidefinite() - assert not network.channel() + assert not network.is_hermitian() + assert not network.is_causal() + assert not network.is_positive_semidefinite() + assert not network.is_channel() assert network.__str__() == "J[4 -> 4]" diff --git a/tests/test_quantum_info_random.py b/tests/test_quantum_info_random.py index bafb4efa84..0749723637 100644 --- a/tests/test_quantum_info_random.py +++ b/tests/test_quantum_info_random.py @@ -223,27 +223,22 @@ def test_random_quantum_channel(backend, representation, measure, rank, order): ) -@pytest.mark.parametrize("haar", [False, True]) @pytest.mark.parametrize("seed", [None, 10, np.random.default_rng(10)]) -def test_random_statevector(backend, haar, seed): +def test_random_statevector(backend, seed): with pytest.raises(TypeError): dims = "10" random_statevector(dims, backend=backend) with pytest.raises(ValueError): dims = 0 random_statevector(dims, backend=backend) - with pytest.raises(TypeError): - dims = 2 - random_statevector(dims, haar=1, backend=backend) with pytest.raises(TypeError): dims = 2 random_statevector(dims, seed=0.1, backend=backend) # tests if random statevector is a pure state dims = 4 - state = random_statevector(dims, haar=haar, seed=seed, backend=backend) - backend.assert_allclose(purity(state) <= 1.0 + PRECISION_TOL, True) - backend.assert_allclose(purity(state) >= 1.0 - PRECISION_TOL, True) + state = random_statevector(dims, seed=seed, backend=backend) + backend.assert_allclose(abs(purity(state) - 1.0) < PRECISION_TOL, True) @pytest.mark.parametrize("normalize", [False, True]) diff --git a/tests/test_quantum_info_utils.py b/tests/test_quantum_info_utils.py index fa121d1eb5..049f800275 100644 --- a/tests/test_quantum_info_utils.py +++ b/tests/test_quantum_info_utils.py @@ -15,8 +15,6 @@ hellinger_distance, hellinger_fidelity, pqc_integral, - shannon_entropy, - total_variation_distance, ) @@ -124,95 +122,6 @@ def test_hadamard_transform(backend, nqubits, implementation, is_matrix): backend.assert_allclose(transformed, test_transformed, atol=PRECISION_TOL) -def test_shannon_entropy_errors(backend): - with pytest.raises(ValueError): - prob = np.array([1.0, 0.0]) - prob = backend.cast(prob, dtype=prob.dtype) - test = shannon_entropy(prob, -2, backend=backend) - with pytest.raises(TypeError): - prob = np.array([[1.0], [0.0]]) - prob = backend.cast(prob, dtype=prob.dtype) - test = shannon_entropy(prob, backend=backend) - with pytest.raises(TypeError): - prob = np.array([]) - prob = backend.cast(prob, dtype=prob.dtype) - test = shannon_entropy(prob, backend=backend) - with pytest.raises(ValueError): - prob = np.array([1.0, -1.0]) - prob = backend.cast(prob, dtype=prob.dtype) - test = shannon_entropy(prob, backend=backend) - with pytest.raises(ValueError): - prob = np.array([1.1, 0.0]) - prob = backend.cast(prob, dtype=prob.dtype) - test = shannon_entropy(prob, backend=backend) - with pytest.raises(ValueError): - prob = np.array([0.5, 0.4999999]) - prob = backend.cast(prob, dtype=prob.dtype) - test = shannon_entropy(prob, backend=backend) - - -@pytest.mark.parametrize("base", [2, 10, np.e, 5]) -def test_shannon_entropy(backend, base): - prob_array = [1.0, 0.0] - result = shannon_entropy(prob_array, base, backend=backend) - backend.assert_allclose(result, 0.0) - - if base == 2: - prob_array = np.array([0.5, 0.5]) - prob_array = backend.cast(prob_array, dtype=prob_array.dtype) - result = shannon_entropy(prob_array, base, backend=backend) - backend.assert_allclose(result, 1.0) - - -@pytest.mark.parametrize("kind", [None, list]) -@pytest.mark.parametrize("validate", [False, True]) -def test_total_variation_distance(backend, validate, kind): - with pytest.raises(TypeError): - prob = np.random.rand(1, 2) - prob_q = np.random.rand(1, 5) - prob = backend.cast(prob, dtype=prob.dtype) - prob_q = backend.cast(prob_q, dtype=prob_q.dtype) - test = total_variation_distance(prob, prob_q, backend=backend) - with pytest.raises(TypeError): - prob = np.random.rand(1, 2)[0] - prob_q = np.array([]) - prob = backend.cast(prob, dtype=prob.dtype) - prob_q = backend.cast(prob_q, dtype=prob_q.dtype) - test = total_variation_distance(prob, prob_q, backend=backend) - with pytest.raises(ValueError): - prob = np.array([-1, 2.0]) - prob_q = np.random.rand(1, 5)[0] - prob = backend.cast(prob, dtype=prob.dtype) - prob_q = backend.cast(prob_q, dtype=prob_q.dtype) - test = total_variation_distance(prob, prob_q, validate=True, backend=backend) - with pytest.raises(ValueError): - prob = np.random.rand(1, 2)[0] - prob_q = np.array([1.0, 0.0]) - prob = backend.cast(prob, dtype=prob.dtype) - prob_q = backend.cast(prob_q, dtype=prob_q.dtype) - test = total_variation_distance(prob, prob_q, validate=True, backend=backend) - with pytest.raises(ValueError): - prob = np.array([1.0, 0.0]) - prob_q = np.random.rand(1, 2)[0] - prob = backend.cast(prob, dtype=prob.dtype) - prob_q = backend.cast(prob_q, dtype=prob_q.dtype) - test = total_variation_distance(prob, prob_q, validate=True, backend=backend) - - prob_p = np.random.rand(10) - prob_q = np.random.rand(10) - prob_p /= np.sum(prob_p) - prob_q /= np.sum(prob_q) - - target = 0.5 * np.sum(np.abs(prob_p - prob_q)) - - if kind is not None: - prob_p, prob_q = kind(prob_p), kind(prob_q) - - distance = total_variation_distance(prob_p, prob_q, validate, backend=backend) - - backend.assert_allclose(distance, target, atol=1e-5) - - @pytest.mark.parametrize("kind", [None, list]) @pytest.mark.parametrize("validate", [False, True]) def test_hellinger(backend, validate, kind): diff --git a/tests/test_transpiler_pipeline.py b/tests/test_transpiler_pipeline.py index ce8afee66d..94d2c4b539 100644 --- a/tests/test_transpiler_pipeline.py +++ b/tests/test_transpiler_pipeline.py @@ -237,6 +237,7 @@ def test_custom_passes_restict(gates, placer, routing): final_layout=final_layout, native_gates=NativeGates.default(), ) + assert transpiled_circ.wire_names == ["q1", "q2", "q3"] def test_custom_passes_multiple_placer(): diff --git a/tests/test_transpiler_placer.py b/tests/test_transpiler_placer.py index 434037e7a4..801304bc31 100644 --- a/tests/test_transpiler_placer.py +++ b/tests/test_transpiler_placer.py @@ -9,6 +9,7 @@ Custom, Random, ReverseTraversal, + StarConnectivityPlacer, Subgraph, Trivial, _find_gates_qubits_pairs, @@ -321,3 +322,25 @@ def test_reverse_traversal_restricted(): assert_placement( circuit=circuit, layout=layout, connectivity=restricted_connectivity ) + + +def test_star_connectivity_placer(): + circ = Circuit(3) + circ.add(gates.CZ(0, 1)) + circ.add(gates.CZ(1, 2)) + circ.add(gates.CZ(0, 2)) + placer = StarConnectivityPlacer(middle_qubit=2) + layout = placer(circ) + assert_placement(circ, layout) + assert layout == {"q0": 0, "q1": 2, "q2": 1} + + +@pytest.mark.parametrize("first", [True, False]) +def test_star_connectivity_placer_error(first): + circ = Circuit(3) + if first: + circ.add(gates.CZ(0, 1)) + circ.add(gates.TOFFOLI(0, 1, 2)) + placer = StarConnectivityPlacer(middle_qubit=2) + with pytest.raises(PlacementError): + layout = placer(circ) diff --git a/tests/test_transpiler_router.py b/tests/test_transpiler_router.py index 716f0f02bd..7a7ef17583 100644 --- a/tests/test_transpiler_router.py +++ b/tests/test_transpiler_router.py @@ -1,24 +1,41 @@ +import itertools + import networkx as nx import numpy as np import pytest from qibo import gates +from qibo.backends import NumpyBackend from qibo.models import Circuit +from qibo.quantum_info.random_ensembles import random_unitary from qibo.transpiler._exceptions import ConnectivityError from qibo.transpiler.optimizer import Preprocessing from qibo.transpiler.pipeline import ( assert_circuit_equivalence, restrict_connectivity_qubits, ) -from qibo.transpiler.placer import Custom, Random, Subgraph, Trivial, assert_placement -from qibo.transpiler.router import CircuitMap, Sabre, ShortestPaths, assert_connectivity +from qibo.transpiler.placer import ( + Custom, + Random, + StarConnectivityPlacer, + Subgraph, + Trivial, + assert_placement, +) +from qibo.transpiler.router import ( + CircuitMap, + Sabre, + ShortestPaths, + StarConnectivityRouter, + assert_connectivity, +) -def star_connectivity(): +def star_connectivity(middle_qubit=2): Q = [i for i in range(5)] chip = nx.Graph() chip.add_nodes_from(Q) - graph_list = [(Q[i], Q[2]) for i in range(5) if i != 2] + graph_list = [(Q[i], Q[middle_qubit]) for i in range(5) if i != middle_qubit] chip.add_edges_from(graph_list) return chip @@ -331,3 +348,55 @@ def test_restrict_qubits(router_algorithm): ) assert_connectivity(restricted_connectivity, routed_circ) assert_placement(routed_circ, final_layout, connectivity=restricted_connectivity) + assert routed_circ.wire_names == ["q0", "q2", "q3"] + + +def test_star_error_multi_qubit(): + circuit = Circuit(3) + circuit.add(gates.TOFFOLI(0, 1, 2)) + transpiler = StarConnectivityRouter(middle_qubit=2) + with pytest.raises(ConnectivityError): + transpiled, hardware_qubits = transpiler( + initial_layout={"q0": 0, "q1": 1, "q2": 2}, circuit=circuit + ) + + +@pytest.mark.parametrize("nqubits", [1, 3, 5]) +@pytest.mark.parametrize("middle_qubit", [0, 2, 4]) +@pytest.mark.parametrize("depth", [2, 10]) +@pytest.mark.parametrize("measurements", [True, False]) +@pytest.mark.parametrize("unitaries", [True, False]) +def test_star_router(nqubits, depth, middle_qubit, measurements, unitaries): + unitary_dim = min(2, nqubits) + connectivity = star_connectivity(middle_qubit) + if unitaries: + circuit = Circuit(nqubits) + pairs = list(itertools.combinations(range(nqubits), unitary_dim)) + for _ in range(depth): + qubits = pairs[int(np.random.randint(len(pairs)))] + circuit.add( + gates.Unitary( + random_unitary(2**unitary_dim, backend=NumpyBackend()), *qubits + ) + ) + else: + circuit = generate_random_circuit(nqubits, depth) + if measurements: + circuit.add(gates.M(0)) + transpiler = StarConnectivityRouter(middle_qubit=middle_qubit) + placer = StarConnectivityPlacer(middle_qubit=middle_qubit) + initial_layout = placer(circuit=circuit) + transpiled_circuit, final_qubit_map = transpiler( + circuit=circuit, initial_layout=initial_layout + ) + assert_connectivity(connectivity, transpiled_circuit) + assert_placement(transpiled_circuit, final_qubit_map) + matched_original = Circuit(max(circuit.nqubits, middle_qubit + 1)) + for gate in circuit.queue: + matched_original.add(gate) + assert_circuit_equivalence( + original_circuit=matched_original, + transpiled_circuit=transpiled_circuit, + final_map=final_qubit_map, + initial_map=initial_layout, + ) diff --git a/tests/test_transpiler_star_connectivity.py b/tests/test_transpiler_star_connectivity.py deleted file mode 100644 index 2330afd76c..0000000000 --- a/tests/test_transpiler_star_connectivity.py +++ /dev/null @@ -1,98 +0,0 @@ -import itertools - -import numpy as np -import pytest - -from qibo import gates -from qibo.backends import NumpyBackend -from qibo.models import Circuit -from qibo.quantum_info.random_ensembles import random_unitary -from qibo.transpiler._exceptions import ConnectivityError -from qibo.transpiler.pipeline import _transpose_qubits -from qibo.transpiler.star_connectivity import StarConnectivity - - -def generate_random_circuit(nqubits, depth, seed=None, middle_qubit=2): - """Generate random circuits one-qubit rotations and CZ gates.""" - # find the number of qubits for hardware circuit - if nqubits == 1: - hardware_qubits = 1 - else: - hardware_qubits = max(nqubits, middle_qubit + 1) - - pairs = list(itertools.combinations(range(hardware_qubits), 2)) - if seed is not None: # pragma: no cover - np.random.seed(seed) - - rotations = [gates.RX, gates.RY, gates.RZ] - circuit = Circuit(hardware_qubits) - for _ in range(depth): - for i in range(hardware_qubits): - # generate a random rotation - rotation = rotations[int(np.random.randint(0, 3))] - theta = 2 * np.pi * np.random.random() - circuit.add(rotation(i, theta=theta)) - # add CZ gates on random qubit pairs - for i in np.random.randint(0, len(pairs), len(pairs)): - q1, q2 = pairs[i] - circuit.add(gates.CZ(q1, q2)) - - return circuit - - -def test_error_multi_qubit(): - circuit = Circuit(3) - circuit.add(gates.TOFFOLI(0, 1, 2)) - transpiler = StarConnectivity(middle_qubit=2) - with pytest.raises(ConnectivityError): - transpiled, hardware_qubits = transpiler(circuit) - - -@pytest.mark.parametrize("nqubits", [1, 2, 3, 4, 5]) -@pytest.mark.parametrize("middle_qubit", [0, 1, 2, 3, 4]) -@pytest.mark.parametrize("depth", [2, 10]) -@pytest.mark.parametrize("measurements", [True, False]) -def test_fix_connectivity(nqubits, depth, middle_qubit, measurements): - """Checks that the transpiled circuit can be executed and is equivalent to original.""" - original = generate_random_circuit(nqubits, depth, middle_qubit=middle_qubit) - if measurements: - original.add(gates.M(0)) - transpiler = StarConnectivity(middle_qubit=middle_qubit) - transpiled, hardware_qubits = transpiler(original) - backend = NumpyBackend() - final_state = backend.execute_circuit(transpiled).state() - target_state = backend.execute_circuit(original).state() - hardware_qubits = list(hardware_qubits.values()) - target_state = _transpose_qubits(target_state, hardware_qubits) - np.testing.assert_allclose(final_state, target_state) - - -@pytest.mark.parametrize("nqubits", [2, 3, 4, 5]) -@pytest.mark.parametrize("middle_qubit", [0, 1, 2, 3, 4]) -@pytest.mark.parametrize("unitary_dim", [1, 2]) -@pytest.mark.parametrize("depth", [2, 10]) -def test_fix_connectivity_unitaries(nqubits, unitary_dim, depth, middle_qubit): - """Checks that the transpiled circuit can be executed and is equivalent to original - when using unitaries.""" - # find the number of qubits for hardware circuit - n_hardware_qubits = max(nqubits, middle_qubit + 1) - - original = Circuit(n_hardware_qubits) - pairs = list(itertools.combinations(range(n_hardware_qubits), unitary_dim)) - for _ in range(depth): - qubits = pairs[int(np.random.randint(len(pairs)))] - original.add( - gates.Unitary( - random_unitary(2**unitary_dim, backend=NumpyBackend()), *qubits - ) - ) - - transpiler = StarConnectivity(middle_qubit=middle_qubit) - transpiled, hardware_qubits = transpiler(original) - # check that execution results agree with original (using simulation) - backend = NumpyBackend() - final_state = backend.execute_circuit(transpiled).state() - target_state = backend.execute_circuit(original).state() - hardware_qubits = list(hardware_qubits.values()) - target_state = _transpose_qubits(target_state, hardware_qubits) - np.testing.assert_allclose(final_state, target_state)