Improvements again

pasqal-io · Oct 10, 2023 · bde4870 · bde4870
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diff --git a/docs/tutorials/qml_tools.md → docs/qml/qml_tools.md b/docs/tutorials/qml_tools.md → docs/qml/qml_tools.md
@@ -1,10 +1,12 @@
-# Quantum Machine Learning Constructors
+# Quantum machine learning constructors
 
-Besides the [arbitrary Hamiltonian constructors](hamiltonians.md), Qadence also provides a complete set of program constructors useful for digital-analog quantum machine learning programs.
+Besides the [arbitrary Hamiltonian constructors](../tutorials/hamiltonians.md), Qadence also provides a complete set of
+program constructors useful for digital-analog quantum machine learning programs.
 
-## Feature Maps
+## Feature maps
 
-A few feature maps are directly available for feature loading,
+A few feature maps are directly available for loading classical data into quantum circuits by encoding them
+into gate rotation angles.
 
 ```python exec="on" source="material-block" result="json" session="fms"
 from qadence import feature_map
@@ -21,9 +23,11 @@ fm = feature_map(n_qubits, fm_type="tower")
 print(f"Tower {fm}") # markdown-exec: hide
 ```
 
-## Hardware-Efficient Ansatz
+## Hardware-efficient ansatz
 
-Ansatze blocks for quantum machine-learning are typically built following the Hardware-Efficient Ansatz formalism (HEA). Both fully digital and digital-analog HEAs can easily be built with the `hea` function. By default, the digital version is returned:
+Ansatze blocks for quantum machine-learning are typically built following the Hardware-Efficient Ansatz formalism (HEA).
+Both fully digital and digital-analog HEAs can easily be built with the `hea` function. By default,
+the digital version is returned:
 
 ```python exec="on" source="material-block" html="1" session="ansatz"
 from qadence import hea
@@ -55,7 +59,7 @@ from qadence.draw import html_string # markdown-exec: hide
 print(html_string(ansatz, size="4,4")) # markdown-exec: hide
 ```
 
-Having a truly *hardware-efficient* ansatz means that the entangling operation can be chosen according to each device's native interactions. Besides digital operations, in Qadence it is also possible to build digital-analog HEAs with the entanglement produced by the natural evolution of a set of interacting qubits, as is natural in neutral atom devices. As with other digital-analog functions, this can be controlled with the `strategy` argument which can be chosen from the [`Strategy`](../qadence/types.md) enum type. Currently, only `Strategy.DIGITAL` and `Strategy.SDAQC` are available. By default, calling `strategy = Strategy.SDAQC` will use a global entangling Hamiltonian with Ising-like NN interactions and constant interaction strength inside a `HamEvo` operation,
+Having a truly *hardware-efficient* ansatz means that the entangling operation can be chosen according to each device's native interactions. Besides digital operations, in Qadence it is also possible to build digital-analog HEAs with the entanglement produced by the natural evolution of a set of interacting qubits, as natively implemented in neutral atom devices. As with other digital-analog functions, this can be controlled with the `strategy` argument which can be chosen from the [`Strategy`](../qadence/types.md) enum type. Currently, only `Strategy.DIGITAL` and `Strategy.SDAQC` are available. By default, calling `strategy = Strategy.SDAQC` will use a global entangling Hamiltonian with Ising-like NN interactions and constant interaction strength inside a `HamEvo` operation,
 
 ```python exec="on" source="material-block" html="1" session="ansatz"
 from qadence import Strategy
@@ -104,6 +108,9 @@ for optimizing fully-differentiable models like `QNN`s and `QuantumModel`s conta
 
 ## Machine Learning Tools
 
+`qadence` also offers a out-of-the-box training routine called `train_with_grad`
+for optimizing fully-differentiable models like `QNN`s and `QuantumModel`s containing either *trainable* and/or *non-trainable* parameters (i.e., inputs). Feel free to [refresh your memory about different parameter types](/tutorials/parameters).
+
 `train_with_grad` performs training, logging/printing loss metrics and storing intermediate checkpoints of models.
 
 As every other training routine commonly used in Machine Learning, it requires

diff --git a/docs/tutorials/ml_tools.md b/docs/tutorials/ml_tools.md
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -16,25 +16,25 @@ nav:
     - Wavefunction Overlaps: tutorials/overlap.md
     - Serialization: tutorials/serializ_and_prep.md
     - Backends: tutorials/backends.md
-    - Tools for Classical and Quantum Machine Learning: tutorials/qml_tools.md
 
   - Digital-Analog Quantum Computing:
     - digital_analog_qc/daqc-basics.md
     - Digital-Analog Emulation:
       - Basics: digital_analog_qc/analog-basics.md
       - Solve a QUBO Problem: digital_analog_qc/analog-qubo.md
-      - Pulse-level Programming with Pulser: digital_analog_qc/pulser-basic.md
+      - Pulse-level programming with Pulser: digital_analog_qc/pulser-basic.md
     - DAQC Transform:
-      - CNOT with Interacting Qubits: digital_analog_qc/daqc-cnot.md
+      - CNOT with interacting qubits: digital_analog_qc/daqc-cnot.md
 
   - Variational Quantum Algorithms:
     - qml/index.md
-    - Quantum Circuit Learning: qml/qcl.md
-    - QAOA for Solving MaxCut: qml/qaoa.md
+    - Quantum circuit learning: qml/qcl.md
+    - Solving MaxCut with QAOA: qml/qaoa.md
+    - Tools for quantum machine learning: qml/qml_tools.md
 
   - Advanced Tutorials:
     - Quantum circuits differentiation: advanced_tutorials/differentiability.md
-    - Custom QuantumModels: advanced_tutorials/custom-models.md
+    - Custom quantum models: advanced_tutorials/custom-models.md
     - Ground-state VQE: advanced_tutorials/vqe.md
 
   - API:
@@ -43,24 +43,24 @@ nav:
     - Register: qadence/register.md
     - QuantumCircuit: qadence/quantumcircuit.md
     - Parameters: qadence/parameters.md
-    - State Preparation: qadence/states.md
+    - State preparation: qadence/states.md
     - Constructors: qadence/constructors.md
     - Transpilation: qadence/transpile.md
     - Execution: qadence/execution.md
-    - Quantum Models: models.md
-    - Machine Learning Tools: qadence/ml_tools.md
+    - Quantum models: models.md
+    - QML tools: qadence/ml_tools.md
     - Serialization: qadence/serialization.md
     - Types: qadence/types.md
 
     - Backends:
-      - Abstract Backend: backends/backend.md
+      - Abstract backend: backends/backend.md
       - PyQTorch: backends/pyqtorch.md
       - Amazon Braket: backends/braket.md
       - Pulser: backends/pulser.md
       - DifferentiableBackend: backends/differentiable.md
 
   - Development:
-    - Architecture and Sharp Bits: development/architecture.md
+    - Architecture and sharp bits: development/architecture.md
     - Drawing: development/draw.md
     - Contributing: development/contributing.md