diff --git a/doc/source/api-reference/qibo.rst b/doc/source/api-reference/qibo.rst
index d877c4c28d..8e4d156269 100644
--- a/doc/source/api-reference/qibo.rst
+++ b/doc/source/api-reference/qibo.rst
@@ -1740,6 +1740,12 @@ Classical relative entropy
 .. autofunction:: qibo.quantum_info.classical_relative_entropy
 
 
+Classical mutual information
+""""""""""""""""""""""""""""
+
+.. autofunction:: qibo.quantum_info.classical_mutual_information
+
+
 Classical Rényi entropy
 """""""""""""""""""""""
 
@@ -1785,6 +1791,12 @@ Relative von Neumann entropy
     an error will be raised when using `cupy` backend.
 
 
+Mutual information
+""""""""""""""""""
+
+.. autofunction:: qibo.quantum_info.mutual_information
+
+
 Rényi entropy
 """""""""""""
 
@@ -1952,6 +1964,12 @@ Matrix exponentiation
 .. autofunction:: qibo.quantum_info.matrix_exponentiation
 
 
+Matrix power
+""""""""""""
+
+.. autofunction:: qibo.quantum_info.matrix_power
+
+
 Quantum Networks
 ^^^^^^^^^^^^^^^^
 
@@ -2386,6 +2404,12 @@ Hellinger shot error
 .. autofunction:: qibo.quantum_info.hellinger_shot_error
 
 
+Total variation distance
+""""""""""""""""""""""""
+
+.. autofunction:: qibo.quantum_info.total_variation_distance
+
+
 Haar integral
 """""""""""""
 
diff --git a/doc/source/code-examples/advancedexamples.rst b/doc/source/code-examples/advancedexamples.rst
index 50ebdef539..e6740b5141 100644
--- a/doc/source/code-examples/advancedexamples.rst
+++ b/doc/source/code-examples/advancedexamples.rst
@@ -1151,7 +1151,7 @@ Additionally, one can also pass single-qubit readout error probabilities (`reado
     )
 
     print("raw circuit:")
-    print(circuit.draw())
+    circuit.draw()
 
     parameters = {
         "t1": {"0": 250*1e-06, "1": 240*1e-06},
@@ -1168,7 +1168,7 @@ Additionally, one can also pass single-qubit readout error probabilities (`reado
     noisy_circuit = noise_model.apply(circuit)
 
     print("noisy circuit:")
-    print(noisy_circuit.draw())
+    noisy_circuit.draw()
 
 .. testoutput::
    :hide:
@@ -1242,7 +1242,7 @@ Let's see how to use them. For starters, let's define a dummy circuit with some
    circ.add(gates.M(*range(nqubits)))
 
    # visualize the circuit
-   print(circ.draw())
+   circ.draw()
 
    #  q0: ─RZ─RX─RZ─RX─RZ─o────o────────M─
    #  q1: ─RZ─RX─RZ─RX─RZ─X─RZ─X─o────o─M─
diff --git a/doc/source/code-examples/examples.rst b/doc/source/code-examples/examples.rst
index 081e8fc71d..ca5bdc190c 100644
--- a/doc/source/code-examples/examples.rst
+++ b/doc/source/code-examples/examples.rst
@@ -310,7 +310,7 @@ For example
     from qibo.models import QFT
 
     c = QFT(5)
-    print(c.draw())
+    c.draw()
     # Prints
     '''
     q0: ─H─U1─U1─U1─U1───────────────────────────x───
diff --git a/doc/source/getting-started/installation.rst b/doc/source/getting-started/installation.rst
index 6d596cba22..8c356b2592 100644
--- a/doc/source/getting-started/installation.rst
+++ b/doc/source/getting-started/installation.rst
@@ -47,18 +47,6 @@ Make sure you have Python 3.9 or greater, then use ``pip`` to install ``qibo`` w
 The ``pip`` program will download and install all the required
 dependencies for Qibo.
 
-Installing with conda
-"""""""""""""""""""""
-
-We provide conda packages for ``qibo`` through the `conda-forge
-<https://anaconda.org/conda-forge>`_ channel.
-
-To install the package with conda run:
-
-.. code-block:: bash
-
-      conda install -c conda-forge qibo
-
 
 Installing from source
 """"""""""""""""""""""
@@ -139,35 +127,6 @@ In order to install the package use the following command:
       `cuQuantum SDK v22.05 <https://docs.nvidia.com/cuda/cuquantum/cuquantum_sdk_release_notes.html#cuquantum-sdk-v22-05>`__.
 
 
-Installing with conda
-"""""""""""""""""""""
-
-We provide conda packages for ``qibo`` and ``qibojit`` through the `conda-forge
-<https://anaconda.org/conda-forge>`_ channel.
-
-To install both packages with conda run:
-
-.. code-block:: bash
-
-      conda install -c conda-forge qibojit
-
-.. note::
-      The ``conda`` program will download and install all the required
-      dependencies except `cupy <https://cupy.dev/>`_ and/or
-      `cuQuantum <https://developer.nvidia.com/cuquantum-sdk>`_
-      which are required for GPU acceleration.
-      The cuQuantum dependency is optional, as it is required only for
-      the ``cuquantum`` platform. Please install `cupy <https://cupy.dev/>`_ by following the
-      instructions from the `official website
-      <https://docs.cupy.dev/en/stable/install.html>`_ for your GPU hardware.
-      The installation instructions for `cuQuantum <https://developer.nvidia.com/cuquantum-sdk>`_
-      are available in the `official documentation <https://docs.nvidia.com/cuda/cuquantum/python/README.html>`__.
-      ``qibojit`` is compatible with
-      `cuQuantum SDK v22.03 <https://docs.nvidia.com/cuda/cuquantum/cuquantum_sdk_release_notes.html#cuquantum-sdk-v22-03>`__
-      and
-      `cuQuantum SDK v22.05 <https://docs.nvidia.com/cuda/cuquantum/cuquantum_sdk_release_notes.html#cuquantum-sdk-v22-05>`__.
-
-
 Installing from source
 """"""""""""""""""""""
 
diff --git a/examples/adiabatic_qml/adiabatic-qml.ipynb b/examples/adiabatic_qml/adiabatic-qml.ipynb
index 0ee08d3d32..66d57a4d19 100644
--- a/examples/adiabatic_qml/adiabatic-qml.ipynb
+++ b/examples/adiabatic_qml/adiabatic-qml.ipynb
@@ -568,7 +568,7 @@
     "circ1 = rotcirc.rotations_circuit(t=0.1)\n",
     "circ2 = rotcirc.rotations_circuit(t=0.8)\n",
     "\n",
-    "print(f\"Circuit diagram: {circ1.draw()}\")\n",
+    "print(f\"Circuit diagram: {str(circ1)}\")\n",
     "print(f\"\\nCirc1 params: {circ1.get_parameters()}\")\n",
     "print(f\"\\nCirc2 params: {circ2.get_parameters()}\")"
    ]
diff --git a/examples/anomaly_detection/test.py b/examples/anomaly_detection/test.py
index 043db58a0c..6104fa6294 100644
--- a/examples/anomaly_detection/test.py
+++ b/examples/anomaly_detection/test.py
@@ -99,7 +99,7 @@ def compute_loss_test(encoder, vector):
     encoder_test = make_encoder(n_qubits, n_layers, trained_params, q_compression)
     encoder_test.compile()
     print("Circuit model summary")
-    print(encoder_test.draw())
+    encoder_test.draw()
 
     print("Computing losses...")
     # Compute loss for standard data
diff --git a/examples/anomaly_detection/train.py b/examples/anomaly_detection/train.py
index 0f662a01f3..0406a1f122 100644
--- a/examples/anomaly_detection/train.py
+++ b/examples/anomaly_detection/train.py
@@ -123,7 +123,7 @@ def train_step(batch_size, encoder, params, dataset):
     # Create and print encoder circuit
     encoder = make_encoder(n_qubits, n_layers, params, q_compression)
     print("Circuit model summary")
-    print(encoder.draw())
+    encoder.draw()
 
     # Define optimizer parameters
     steps_for_epoch = math.ceil(train_size / batch_size)
diff --git a/examples/circuit-draw-mpl/qibo-draw-circuit-matplotlib.ipynb b/examples/circuit-draw-mpl/qibo-draw-circuit-matplotlib.ipynb
index 754592147e..a9ae2410e1 100644
--- a/examples/circuit-draw-mpl/qibo-draw-circuit-matplotlib.ipynb
+++ b/examples/circuit-draw-mpl/qibo-draw-circuit-matplotlib.ipynb
@@ -101,7 +101,7 @@
     "    \n",
     "ansatz.add((gates.RY(q, theta=0) for q in range(nqubits)))\n",
     "ansatz.add(gates.M(qubit) for qubit in range(2))\n",
-    "print(ansatz.draw())"
+    "ansatz.draw()"
    ]
   },
   {
@@ -185,7 +185,7 @@
     "c.add(gates.X(1))\n",
     "c.add(gates.X(0))\n",
     "c.add(gates.M(qubit) for qubit in range(2))\n",
-    "print(c.draw())"
+    "c.draw()"
    ]
   },
   {
@@ -206,7 +206,7 @@
     }
    ],
    "source": [
-    "plot_circuit(c);"
+    "plot_circuit(c)"
    ]
   },
   {
@@ -231,7 +231,7 @@
     "c = QFT(5)\n",
     "c.add(gates.M(qubit) for qubit in range(2))\n",
     "\n",
-    "print(c.draw())"
+    "c.draw()"
    ]
   },
   {
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "id": "916f7b83-1ad7-4984-8573-eb55dfeb125d",
    "metadata": {},
    "outputs": [
@@ -384,7 +384,7 @@
     }
    ],
    "source": [
-    "print(c.fuse().draw())"
+    "c.fuse().draw()"
    ]
   },
   {
@@ -440,7 +440,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fa46e167",
+   "id": "626f9d58",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/examples/dbi/dbi_tutorial_basic_intro.ipynb b/examples/dbi/dbi_tutorial_basic_intro.ipynb
index 425684b910..5a722341ea 100644
--- a/examples/dbi/dbi_tutorial_basic_intro.ipynb
+++ b/examples/dbi/dbi_tutorial_basic_intro.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "62d9723f",
    "metadata": {},
    "outputs": [],
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "b80b4738",
    "metadata": {},
    "outputs": [],
@@ -38,7 +38,7 @@
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "\n",
-    "from hyperopt import hp, tpe\n",
+    "import optuna\n",
     "\n",
     "from qibo import hamiltonians, set_backend\n",
     "from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType, DoubleBracketIteration, DoubleBracketScheduling"
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "933d9a00",
    "metadata": {},
    "outputs": [],
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "7125940f",
    "metadata": {},
    "outputs": [
@@ -114,7 +114,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Qibo 0.2.9|INFO|2024-07-03 11:29:11]: Using numpy backend on /CPU:0\n"
+      "[Qibo 0.2.12|INFO|2024-09-06 12:03:17]: Using numpy backend on /CPU:0\n"
      ]
     },
     {
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "1adafc19",
    "metadata": {},
    "outputs": [
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "8a4d0e9d",
    "metadata": {},
    "outputs": [],
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "9521c464",
    "metadata": {},
    "outputs": [],
@@ -227,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "290e5828",
    "metadata": {},
    "outputs": [
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "3e2b9950",
    "metadata": {},
    "outputs": [
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "638ba4b5",
    "metadata": {},
    "outputs": [
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "08f0c466",
    "metadata": {},
    "outputs": [
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "90e6fdff",
    "metadata": {},
    "outputs": [
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "0d90c8b5",
    "metadata": {},
    "outputs": [
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "13710cc2",
    "metadata": {},
    "outputs": [
@@ -399,7 +399,7 @@
        "6.708203932499369"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "a7749a96",
    "metadata": {},
    "outputs": [
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "fc01baa4",
    "metadata": {},
    "outputs": [
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "0d7b86d3",
    "metadata": {},
    "outputs": [],
@@ -503,15 +503,14 @@
     "    scheduling=DoubleBracketScheduling.hyperopt,\n",
     "    step_min = 1e-5,\n",
     "    step_max = 1,\n",
-    "    space = hp.uniform,\n",
-    "    optimizer = tpe,\n",
+    "    optimizer = optuna.samplers.TPESampler(),\n",
     "    max_evals = 1000,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "1b9b1431",
    "metadata": {},
    "outputs": [
@@ -532,13 +531,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "52fa3599",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x500 with 2 Axes>"
       ]
@@ -565,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "d1f197b1",
    "metadata": {},
    "outputs": [],
@@ -588,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "c115c222",
    "metadata": {},
    "outputs": [
@@ -625,25 +624,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "New optimized step at iteration 1/20: 0.012285383759689142\n",
-      "New optimized step at iteration 2/20: 0.008447438941779167\n",
-      "New optimized step at iteration 3/20: 0.007844606075793771\n",
-      "New optimized step at iteration 4/20: 0.005843560327876751\n",
-      "New optimized step at iteration 5/20: 0.010251929595700982\n",
-      "New optimized step at iteration 6/20: 0.005841082475272156\n",
-      "New optimized step at iteration 7/20: 0.004631288000301592\n",
-      "New optimized step at iteration 8/20: 0.006817597467464319\n",
-      "New optimized step at iteration 9/20: 0.010226644173464028\n",
-      "New optimized step at iteration 10/20: 0.007818628910859757\n",
-      "New optimized step at iteration 11/20: 0.005826089136496147\n",
-      "New optimized step at iteration 12/20: 0.016243987144888304\n",
-      "New optimized step at iteration 13/20: 0.0012186965495374139\n",
-      "New optimized step at iteration 14/20: 0.0013670062367486478\n",
-      "New optimized step at iteration 15/20: 0.014667317020784512\n",
-      "New optimized step at iteration 16/20: 0.007361713849428013\n",
-      "New optimized step at iteration 17/20: 0.03387196372964355\n",
-      "New optimized step at iteration 18/20: 0.00385715363849674\n",
-      "New optimized step at iteration 19/20: 0.00441489412311609\n"
+      "New optimized step at iteration 1/20: 0.00934294935664311\n",
+      "New optimized step at iteration 2/20: 0.009364588177233102\n",
+      "New optimized step at iteration 3/20: 0.005985356940597437\n",
+      "New optimized step at iteration 4/20: 0.011472840984366184\n",
+      "New optimized step at iteration 5/20: 0.006802887431910996\n",
+      "New optimized step at iteration 6/20: 0.010837702507351613\n",
+      "New optimized step at iteration 7/20: 0.006624471861894687\n",
+      "New optimized step at iteration 8/20: 0.00870720701470905\n",
+      "New optimized step at iteration 9/20: 0.005748706054245771\n",
+      "New optimized step at iteration 10/20: 0.009512049459920756\n",
+      "New optimized step at iteration 11/20: 0.004887478565382978\n",
+      "New optimized step at iteration 12/20: 0.011309993175156744\n",
+      "New optimized step at iteration 13/20: 0.0017896288977535153\n",
+      "New optimized step at iteration 14/20: 0.0003944795659594491\n",
+      "New optimized step at iteration 15/20: 0.0006390700306615794\n",
+      "New optimized step at iteration 16/20: 0.0008772593599309826\n",
+      "New optimized step at iteration 17/20: 0.012559015937191706\n",
+      "New optimized step at iteration 18/20: 0.003294180889937215\n",
+      "New optimized step at iteration 19/20: 0.002707744316510693\n"
      ]
     }
    ],
@@ -659,9 +658,8 @@
     "step = dbf_2.choose_step(\n",
     "    step_min = 1e-5,\n",
     "    step_max = 1,\n",
-    "    space = hp.uniform,\n",
-    "    optimizer = tpe,\n",
-    "    max_evals = 500,\n",
+    "    optimizer = optuna.samplers.TPESampler(),\n",
+    "    max_evals = 1000,\n",
     ")\n",
     "\n",
     "for s in range(NSTEPS):\n",
@@ -669,9 +667,8 @@
     "        step = dbf_2.choose_step(\n",
     "            step_min = 1e-5,\n",
     "            step_max = 1,\n",
-    "            space = hp.uniform,\n",
-    "            optimizer = tpe,\n",
-    "            max_evals = 100,\n",
+    "            optimizer = optuna.samplers.TPESampler(),\n",
+    "            max_evals = 1000,\n",
     "        )\n",
     "        print(f\"New optimized step at iteration {s}/{NSTEPS}: {step}\")\n",
     "    dbf_2(step=step)\n",
@@ -689,7 +686,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x375 with 1 Axes>"
       ]
@@ -751,6 +748,14 @@
    "source": [
     "visualize_matrix(dbf_2.h.matrix)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "310c0bad-4eeb-4940-8c18-5921dfb4d157",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/qcnn_classifier/qcnn_demo.ipynb b/examples/qcnn_classifier/qcnn_demo.ipynb
index 0f72938bab..6b372e22e1 100644
--- a/examples/qcnn_classifier/qcnn_demo.ipynb
+++ b/examples/qcnn_classifier/qcnn_demo.ipynb
@@ -240,7 +240,7 @@
    "source": [
     "test = QuantumCNN(nqubits=4, nlayers=1, nclasses=2)\n",
     "testcircuit = test._circuit\n",
-    "print(testcircuit.draw())"
+    "testcircuit.draw()"
    ]
   },
   {
diff --git a/examples/qfiae/qfiae_demo.ipynb b/examples/qfiae/qfiae_demo.ipynb
index 59d78b008d..04f118d5ba 100644
--- a/examples/qfiae/qfiae_demo.ipynb
+++ b/examples/qfiae/qfiae_demo.ipynb
@@ -359,7 +359,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1500x700 with 1 Axes>"
       ]
@@ -464,7 +464,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -655,7 +655,7 @@
    ],
    "source": [
     "circuit_a_prob = create_circuit_a(n_qubits = 4,b_max = x_max_int, b_min = x_min_int)\n",
-    "print(circuit_a_prob.draw())"
+    "circuit_a_prob.draw()"
    ]
   },
   {
@@ -753,7 +753,7 @@
     }
    ],
    "source": [
-    "print(create_circuit_q(n_qubits=4,circuit_a=circuit_a_prob).draw())"
+    "create_circuit_q(n_qubits=4,circuit_a=circuit_a_prob).draw()"
    ]
   },
   {
diff --git a/poetry.lock b/poetry.lock
index 30cb114edc..a995eca8f6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -22,6 +22,25 @@ files = [
     {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"},
 ]
 
+[[package]]
+name = "alembic"
+version = "1.13.2"
+description = "A database migration tool for SQLAlchemy."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "alembic-1.13.2-py3-none-any.whl", hash = "sha256:6b8733129a6224a9a711e17c99b08462dbf7cc9670ba8f2e2ae9af860ceb1953"},
+    {file = "alembic-1.13.2.tar.gz", hash = "sha256:1ff0ae32975f4fd96028c39ed9bb3c867fe3af956bd7bb37343b54c9fe7445ef"},
+]
+
+[package.dependencies]
+Mako = "*"
+SQLAlchemy = ">=1.3.0"
+typing-extensions = ">=4"
+
+[package.extras]
+tz = ["backports.zoneinfo"]
+
 [[package]]
 name = "antlr4-python3-runtime"
 version = "4.13.2"
@@ -200,100 +219,100 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "cachetools"
-version = "5.4.0"
+version = "5.5.0"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.4.0-py3-none-any.whl", hash = "sha256:3ae3b49a3d5e28a77a0be2b37dbcb89005058959cb2323858c2657c4a8cab474"},
-    {file = "cachetools-5.4.0.tar.gz", hash = "sha256:b8adc2e7c07f105ced7bc56dbb6dfbe7c4a00acce20e2227b3f355be89bc6827"},
+    {file = "cachetools-5.5.0-py3-none-any.whl", hash = "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292"},
+    {file = "cachetools-5.5.0.tar.gz", hash = "sha256:2cc24fb4cbe39633fb7badd9db9ca6295d766d9c2995f245725a46715d050f2a"},
 ]
 
 [[package]]
 name = "certifi"
-version = "2024.7.4"
+version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
-    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
 ]
 
 [[package]]
 name = "cffi"
-version = "1.17.0"
+version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "cffi-1.17.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f9338cc05451f1942d0d8203ec2c346c830f8e86469903d5126c1f0a13a2bcbb"},
-    {file = "cffi-1.17.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a0ce71725cacc9ebf839630772b07eeec220cbb5f03be1399e0457a1464f8e1a"},
-    {file = "cffi-1.17.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c815270206f983309915a6844fe994b2fa47e5d05c4c4cef267c3b30e34dbe42"},
-    {file = "cffi-1.17.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6bdcd415ba87846fd317bee0774e412e8792832e7805938987e4ede1d13046d"},
-    {file = "cffi-1.17.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8a98748ed1a1df4ee1d6f927e151ed6c1a09d5ec21684de879c7ea6aa96f58f2"},
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 [package.dependencies]
@@ -549,17 +568,6 @@ files = [
 [package.dependencies]
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-version = "3.0.0"
-description = "Pickler class to extend the standard pickle.Pickler functionality"
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 version = "3.4.0"
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 ]
 
+[[package]]
+name = "colorlog"
+version = "6.8.2"
+description = "Add colours to the output of Python's logging module."
+optional = false
+python-versions = ">=3.6"
+files = [
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+
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+
+[package.extras]
+development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
+
 [[package]]
 name = "comm"
 version = "0.2.2"
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 description = "Python library for calculating contours of 2D quadrilateral grids"
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 [package.dependencies]
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 [package.extras]
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-mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"]
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 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
-test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "cotengra"
@@ -1230,13 +1276,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
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+version = "2.1.0"
 description = "Get the currently executing AST node of a frame, and other information"
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 [[package]]
 name = "flatbuffers"
@@ -1449,13 +1495,13 @@ woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "fsspec"
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 python-versions = ">=3.8"
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 [package.extras]
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 sphinx = ">=5.0,<7.0"
 sphinx-basic-ng = "*"
 
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-version = "1.0.0"
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-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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 [[package]]
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 version = "0.6.0"
@@ -1527,13 +1562,13 @@ files = [
 
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 description = "Google API client core library"
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -1558,13 +1593,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.33.0"
+version = "2.34.0"
 description = "Google Authentication Library"
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 python-versions = ">=3.7"
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 pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
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 [[package]]
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 [package.dependencies]
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 [package.extras]
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 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.65.4)"]
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 [[package]]
 name = "grpcio-status"
@@ -1687,18 +1801,18 @@ protobuf = ">=4.21.6"
 
 [[package]]
 name = "grpcio-status"
-version = "1.65.4"
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 description = "Status proto mapping for gRPC"
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 python-versions = ">=3.8"
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 [package.dependencies]
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-grpcio = ">=1.65.4"
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 protobuf = ">=5.26.1,<6.0dev"
 
 [[package]]
@@ -1789,44 +1903,20 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<13)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 
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-six = "*"
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-
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-mongotrials = ["pymongo"]
-sparktrials = ["pyspark"]
-
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
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 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "imagesize"
 version = "1.4.1"
@@ -1840,40 +1930,48 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "8.2.0"
+version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+zipp = ">=3.20"
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy"]
 
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 name = "importlib-resources"
-version = "6.4.2"
+version = "6.4.5"
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 python-versions = ">=3.8"
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 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
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+cover = ["pytest-cov"]
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+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "iniconfig"
@@ -1972,21 +2070,21 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pa
 
 [[package]]
 name = "ipywidgets"
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+version = "8.1.5"
 description = "Jupyter interactive widgets"
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-jupyterlab-widgets = ">=3.0.11,<3.1.0"
+jupyterlab-widgets = ">=3.0.12,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.11,<4.1.0"
+widgetsnbextension = ">=4.0.12,<4.1.0"
 
 [package.extras]
 test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
@@ -2084,13 +2182,13 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.2"
+version = "8.6.3"
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 optional = false
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 [package.dependencies]
@@ -2138,13 +2236,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.11"
+version = "3.0.13"
 description = "Jupyter interactive widgets for JupyterLab"
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 [[package]]
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+
+[package.extras]
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+lingua = ["lingua"]
+testing = ["pytest"]
+
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 [package.dependencies]
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 [[package]]
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 test = ["pytest", "pytest-cov", "pytest-xdist"]
 torch = ["torch"]
 
+[[package]]
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+
 [[package]]
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 version = "24.1"
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+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "pluggy"
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 ]
 
 [package.dependencies]
@@ -4301,7 +4453,7 @@ develop = false
 [package.dependencies]
 numba = ">=0.59.0"
 psutil = "^5.9.5"
-qibo = "^0.2.10"
+qibo = {git = "https://github.com/qiboteam/qibo.git", branch = "matrix_power"}
 scipy = "^1.10.1"
 
 [package.extras]
@@ -4312,7 +4464,7 @@ cuquantum = ["cuquantum-python-cu12 (>=23.10.0,<24.0.0)"]
 type = "git"
 url = "https://github.com/qiboteam/qibojit.git"
 reference = "HEAD"
-resolved_reference = "0d9bbd2f0f3545293cdb2ac039680b8dbceaf55e"
+resolved_reference = "5dd522f84bdd2a66d67b41df990beb3135b3a9f5"
 
 [[package]]
 name = "qibotn"
@@ -4334,7 +4486,7 @@ cuda = ["cupy-cuda11x (>=11.6.0,<12.0.0)", "cuquantum-python-cu11 (>=23.3.0,<24.
 type = "git"
 url = "https://github.com/qiboteam/qibotn.git"
 reference = "HEAD"
-resolved_reference = "adb3875d235a002d9782691c9e62592054e603af"
+resolved_reference = "6590d9b64dff324bcf805c9502e3d558e1c88cf9"
 
 [[package]]
 name = "quimb"
@@ -4367,30 +4519,32 @@ tests = ["coverage", "pytest", "pytest-cov"]
 
 [[package]]
 name = "qulacs"
-version = "0.6.4.1"
+version = "0.6.10"
 description = "Quantum circuit simulator for research"
 optional = false
 python-versions = "*"
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 [package.dependencies]
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 [package.extras]
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 test = ["openfermion"]
 
 [[package]]
@@ -4484,13 +4638,13 @@ idna2008 = ["idna"]
 
 [[package]]
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 [package.dependencies]
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 ]
 
 [package.dependencies]
@@ -4646,11 +4800,11 @@ threadpoolctl = ">=3.1.0"
 [package.extras]
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 [[package]]
 name = "scipy"
@@ -4717,18 +4871,23 @@ stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
 name = "setuptools"
-version = "70.3.0"
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 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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+cover = ["pytest-cov"]
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+type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
 
 [[package]]
 name = "six"
@@ -4981,6 +5140,93 @@ lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
+[[package]]
+name = "sqlalchemy"
+version = "2.0.35"
+description = "Database Abstraction Library"
+optional = false
+python-versions = ">=3.7"
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+    {file = "sqlalchemy-2.0.35.tar.gz", hash = "sha256:e11d7ea4d24f0a262bccf9a7cd6284c976c5369dac21db237cff59586045ab9f"},
+]
+
+[package.dependencies]
+greenlet = {version = "!=0.4.17", markers = "python_version < \"3.13\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
+aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
+asyncio = ["greenlet (!=0.4.17)"]
+asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
+mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
+mssql = ["pyodbc"]
+mssql-pymssql = ["pymssql"]
+mssql-pyodbc = ["pyodbc"]
+mypy = ["mypy (>=0.910)"]
+mysql = ["mysqlclient (>=1.4.0)"]
+mysql-connector = ["mysql-connector-python"]
+oracle = ["cx_oracle (>=8)"]
+oracle-oracledb = ["oracledb (>=1.0.1)"]
+postgresql = ["psycopg2 (>=2.7)"]
+postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
+postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
+postgresql-psycopg = ["psycopg (>=3.0.7)"]
+postgresql-psycopg2binary = ["psycopg2-binary"]
+postgresql-psycopg2cffi = ["psycopg2cffi"]
+postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
+pymysql = ["pymysql"]
+sqlcipher = ["sqlcipher3_binary"]
+
 [[package]]
 name = "stack-data"
 version = "0.6.3"
@@ -5455,13 +5701,13 @@ files = [
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20240316"
+version = "2.9.0.20240906"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
-    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
+    {file = "types-python-dateutil-2.9.0.20240906.tar.gz", hash = "sha256:9706c3b68284c25adffc47319ecc7947e5bb86b3773f843c73906fd598bc176e"},
+    {file = "types_python_dateutil-2.9.0.20240906-py3-none-any.whl", hash = "sha256:27c8cc2d058ccb14946eebcaaa503088f4f6dbc4fb6093d3d456a49aef2753f6"},
 ]
 
 [[package]]
@@ -5499,13 +5745,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.2.2"
+version = "2.2.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
-    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
+    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
+    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
 ]
 
 [package.extras]
@@ -5538,13 +5784,13 @@ files = [
 
 [[package]]
 name = "werkzeug"
-version = "3.0.3"
+version = "3.0.4"
 description = "The comprehensive WSGI web application library."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
-    {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
+    {file = "werkzeug-3.0.4-py3-none-any.whl", hash = "sha256:02c9eb92b7d6c06f31a782811505d2157837cea66aaede3e217c7c27c039476c"},
+    {file = "werkzeug-3.0.4.tar.gz", hash = "sha256:34f2371506b250df4d4f84bfe7b0921e4762525762bbd936614909fe25cd7306"},
 ]
 
 [package.dependencies]
@@ -5569,13 +5815,13 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.11"
+version = "4.0.13"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
-    {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
+    {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"},
+    {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
 ]
 
 [[package]]
@@ -5676,18 +5922,22 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.20.0"
+version = "3.20.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.20.0-py3-none-any.whl", hash = "sha256:58da6168be89f0be59beb194da1250516fdaa062ccebd30127ac65d30045e10d"},
-    {file = "zipp-3.20.0.tar.gz", hash = "sha256:0145e43d89664cfe1a2e533adc75adafed82fe2da404b4bbb6b026c0157bdb31"},
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
 ]
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
 
 [extras]
 qinfo = []
@@ -5698,4 +5948,4 @@ torch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "712016dc85e542db06578a3bb94c1bc58ccf533113ab7e8f40569d6225c87d73"
+content-hash = "56a9714596912650409692687d8983633a31ad11ec488b33429d523088700d5b"
diff --git a/pyproject.toml b/pyproject.toml
index e9b9af39be..c9d614fc3a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "qibo"
-version = "0.2.12"
+version = "0.2.13"
 description = "A framework for quantum computing with hardware acceleration."
 authors = ["The Qibo team"]
 license = "Apache License 2.0"
@@ -26,9 +26,7 @@ scipy = "^1.10.1"
 sympy = "^1.11.1"
 cma = "^3.3.0"
 joblib = "^1.2.0"
-hyperopt = "^0.2.7"
-# `setuptools` is only required because undeclared by `hyperopt`
-setuptools = ">=69.1.1,<71.0.0"
+optuna = "^4.0.0"
 tabulate = "^0.9.0"
 openqasm3 = { version = ">=0.5.0", extras = ["parser"] }
 numpy = "^1.26.4"
diff --git a/src/qibo/backends/_clifford_operations.py b/src/qibo/backends/_clifford_operations.py
index 9030123e57..1980b8d230 100644
--- a/src/qibo/backends/_clifford_operations.py
+++ b/src/qibo/backends/_clifford_operations.py
@@ -388,10 +388,12 @@ def _rowsum(symplectic_matrix, h, i, nqubits, determined=False):
 def _determined_outcome(state, q, nqubits):
     state[-1, :] = 0
     idx = (state[:nqubits, q].nonzero()[0] + nqubits).astype(np.uint)
+    if len(idx) == 0:
+        return state, state[-1, -1]
     state = _pack_for_measurements(state, nqubits)
     state = _rowsum(
         state,
-        2 * nqubits * np.ones(idx.shape, dtype=np.uint),
+        _dim_xz(nqubits) * np.ones(idx.shape, dtype=np.uint),
         idx,
         _packed_size(nqubits),
         True,
@@ -427,7 +429,14 @@ def _random_outcome(state, p, q, nqubits):
 @cache
 def _dim(nqubits):
     """Returns the dimension of the symplectic matrix for a given number of qubits."""
-    return 2 * nqubits + 1
+    return _dim_xz(nqubits) + 1
+
+
+@cache
+def _dim_xz(nqubits):
+    """Returns the dimension of the symplectic matrix (only the de/stabilizers generators part,
+    without the phases and scratch row) for a given number of qubits."""
+    return 2 * nqubits
 
 
 @cache
@@ -440,8 +449,8 @@ def _packbits(array, axis):
     return np.packbits(array, axis=axis)
 
 
-def _unpackbits(array, axis):
-    return np.unpackbits(array, axis=axis)
+def _unpackbits(array, axis, count):
+    return np.unpackbits(array, axis=axis, count=count)
 
 
 def _pack_for_measurements(state, nqubits):
@@ -452,23 +461,16 @@ def _pack_for_measurements(state, nqubits):
     return np.hstack((x, z, r[:, None]))
 
 
-@cache
-def _pad_size(n):
-    """Returns the size of the pad added to an array of original dimension `n` after unpacking."""
-    return 8 - (n % 8)
-
-
 def _unpack_for_measurements(state, nqubits):
     """Unpacks the symplectc matrix that was packed for measurements."""
-    xz = _unpackbits(state[:, :-1], axis=1)
-    padding_size = _pad_size(nqubits)
-    x, z = xz[:, :nqubits], xz[:, nqubits + padding_size : -padding_size]
+    xz = _unpackbits(state[:, :-1], axis=1, count=_dim_xz(nqubits))
+    x, z = xz[:, :nqubits], xz[:, nqubits:]
     return np.hstack((x, z, state[:, -1][:, None]))
 
 
 def _init_state_for_measurements(state, nqubits, collapse):
     if collapse:
-        return _unpackbits(state, axis=0)[: _dim(nqubits)]
+        return _unpackbits(state, axis=0, count=_dim_xz(nqubits))[: _dim(nqubits)]
     else:
         return state.copy()
 
@@ -523,7 +525,7 @@ def _clifford_post_execution_reshape(state, nqubits: int):
     Returns:
         (np.array) The unpacked and reshaped state.
     """
-    state = _unpackbits(state, axis=0)[: _dim(nqubits)]
+    state = _unpackbits(state, axis=0, count=_dim(nqubits))[: _dim(nqubits)]
     return state
 
 
diff --git a/src/qibo/backends/abstract.py b/src/qibo/backends/abstract.py
index abe1f20d83..468fa89008 100644
--- a/src/qibo/backends/abstract.py
+++ b/src/qibo/backends/abstract.py
@@ -1,4 +1,5 @@
 import abc
+from typing import Union
 
 from qibo.config import raise_error
 
@@ -318,12 +319,16 @@ def calculate_overlap_density_matrix(self, state1, state2):  # pragma: no cover
         raise_error(NotImplementedError)
 
     @abc.abstractmethod
-    def calculate_eigenvalues(self, matrix, k=6):  # pragma: no cover
+    def calculate_eigenvalues(
+        self, matrix, k: int = 6, hermitian: bool = True
+    ):  # pragma: no cover
         """Calculate eigenvalues of a matrix."""
         raise_error(NotImplementedError)
 
     @abc.abstractmethod
-    def calculate_eigenvectors(self, matrix, k=6):  # pragma: no cover
+    def calculate_eigenvectors(
+        self, matrix, k: int = 6, hermitian: bool = True
+    ):  # pragma: no cover
         """Calculate eigenvectors of a matrix."""
         raise_error(NotImplementedError)
 
@@ -351,6 +356,21 @@ def calculate_matrix_exp(
         """
         raise_error(NotImplementedError)
 
+    @abc.abstractmethod
+    def calculate_matrix_power(
+        self, matrix, power: Union[float, int]
+    ):  # pragma: no cover
+        """Calculate the (fractional) ``power`` :math:`\\alpha` of ``matrix`` :math:`A`,
+        i.e. :math:`A^{\\alpha}`.
+
+        .. note::
+            For the ``pytorch`` backend, this method relies on a copy of the original tensor.
+            This may break the gradient flow. For the GPU backends (i.e. ``cupy`` and
+            ``cuquantum``), this method falls back to CPU whenever ``power`` is not
+            an integer.
+        """
+        raise_error(NotImplementedError)
+
     @abc.abstractmethod
     def calculate_hamiltonian_matrix_product(
         self, matrix1, matrix2
diff --git a/src/qibo/backends/numpy.py b/src/qibo/backends/numpy.py
index bcde6ac013..fe1441b7c6 100644
--- a/src/qibo/backends/numpy.py
+++ b/src/qibo/backends/numpy.py
@@ -1,9 +1,10 @@
 import collections
 import math
+from typing import Union
 
 import numpy as np
 from scipy import sparse
-from scipy.linalg import block_diag
+from scipy.linalg import block_diag, fractional_matrix_power
 
 from qibo import __version__
 from qibo.backends import einsum_utils
@@ -719,7 +720,7 @@ def calculate_overlap_density_matrix(self, state1, state2):
             self.np.matmul(self.np.conj(self.cast(state1)).T, self.cast(state2))
         )
 
-    def calculate_eigenvalues(self, matrix, k=6, hermitian=True):
+    def calculate_eigenvalues(self, matrix, k: int = 6, hermitian: bool = True):
         if self.is_sparse(matrix):
             log.warning(
                 "Calculating sparse matrix eigenvectors because "
@@ -730,7 +731,7 @@ def calculate_eigenvalues(self, matrix, k=6, hermitian=True):
             return np.linalg.eigvalsh(matrix)
         return np.linalg.eigvals(matrix)
 
-    def calculate_eigenvectors(self, matrix, k=6, hermitian=True):
+    def calculate_eigenvectors(self, matrix, k: int = 6, hermitian: bool = True):
         if self.is_sparse(matrix):
             if k < matrix.shape[0]:
                 from scipy.sparse.linalg import eigsh
@@ -768,6 +769,14 @@ def calculate_matrix_exp(self, a, matrix, eigenvectors=None, eigenvalues=None):
         ud = self.np.transpose(np.conj(eigenvectors))
         return self.np.matmul(eigenvectors, self.np.matmul(expd, ud))
 
+    def calculate_matrix_power(self, matrix, power: Union[float, int]):
+        if not isinstance(power, (float, int)):
+            raise_error(
+                TypeError,
+                f"``power`` must be either float or int, but it is type {type(power)}.",
+            )
+        return fractional_matrix_power(matrix, power)
+
     # TODO: remove this method
     def calculate_hamiltonian_matrix_product(self, matrix1, matrix2):
         return matrix1 @ matrix2
diff --git a/src/qibo/backends/pytorch.py b/src/qibo/backends/pytorch.py
index 392aeed405..22fbbc5640 100644
--- a/src/qibo/backends/pytorch.py
+++ b/src/qibo/backends/pytorch.py
@@ -120,7 +120,11 @@ def cast(
 
         if isinstance(x, self.np.Tensor):
             x = x.to(dtype)
-        elif isinstance(x, list) and all(isinstance(row, self.np.Tensor) for row in x):
+        elif (
+            isinstance(x, list)
+            and len(x) > 0
+            and all(isinstance(row, self.np.Tensor) for row in x)
+        ):
             x = self.np.stack(x)
         else:
             x = self.np.tensor(x, dtype=dtype, requires_grad=requires_grad)
@@ -176,12 +180,12 @@ def sample_shots(self, probabilities, nshots):
             self.cast(probabilities, dtype="float"), nshots, replacement=True
         )
 
-    def calculate_eigenvalues(self, matrix, k=6, hermitian=True):
+    def calculate_eigenvalues(self, matrix, k: int = 6, hermitian: bool = True):
         if hermitian:
             return self.np.linalg.eigvalsh(matrix)  # pylint: disable=not-callable
         return self.np.linalg.eigvals(matrix)  # pylint: disable=not-callable
 
-    def calculate_eigenvectors(self, matrix, k=6, hermitian=True):
+    def calculate_eigenvectors(self, matrix, k: int = 6, hermitian: int = True):
         if hermitian:
             return self.np.linalg.eigh(matrix)  # pylint: disable=not-callable
         return self.np.linalg.eig(matrix)  # pylint: disable=not-callable
@@ -195,6 +199,11 @@ def calculate_matrix_exp(self, a, matrix, eigenvectors=None, eigenvalues=None):
         ud = self.np.conj(eigenvectors).T
         return self.np.matmul(eigenvectors, self.np.matmul(expd, ud))
 
+    def calculate_matrix_power(self, matrix, power):
+        copied = self.to_numpy(self.np.copy(matrix))
+        copied = super().calculate_matrix_power(copied, power)
+        return self.cast(copied, dtype=copied.dtype)
+
     def _test_regressions(self, name):
         if name == "test_measurementresult_apply_bitflips":
             return [
diff --git a/src/qibo/backends/tensorflow.py b/src/qibo/backends/tensorflow.py
index 6f4deed49f..2088cd69be 100644
--- a/src/qibo/backends/tensorflow.py
+++ b/src/qibo/backends/tensorflow.py
@@ -177,12 +177,12 @@ def calculate_norm_density_matrix(self, state, order="nuc"):
             return self.np.trace(state)
         return self.tf.norm(state, ord=order)
 
-    def calculate_eigenvalues(self, matrix, k=6, hermitian=True):
+    def calculate_eigenvalues(self, matrix, k: int = 6, hermitian: bool = True):
         if hermitian:
             return self.tf.linalg.eigvalsh(matrix)
         return self.tf.linalg.eigvals(matrix)
 
-    def calculate_eigenvectors(self, matrix, k=6, hermitian=True):
+    def calculate_eigenvectors(self, matrix, k: int = 6, hermitian: bool = True):
         if hermitian:
             return self.tf.linalg.eigh(matrix)
         return self.tf.linalg.eig(matrix)
diff --git a/src/qibo/gates/abstract.py b/src/qibo/gates/abstract.py
index 250c309934..3429cb3c8a 100644
--- a/src/qibo/gates/abstract.py
+++ b/src/qibo/gates/abstract.py
@@ -268,7 +268,7 @@ def on_qubits(self, qubit_map) -> "Gate":
                 c.add(gates.CNOT(2, 3).on_qubits({2: 3, 3: 0})) # equivalent to gates.CNOT(3, 0)
                 c.add(gates.CNOT(2, 3).on_qubits({2: 1, 3: 3})) # equivalent to gates.CNOT(1, 3)
                 c.add(gates.CNOT(2, 3).on_qubits({2: 2, 3: 1})) # equivalent to gates.CNOT(2, 1)
-                print(c.draw())
+                c.draw()
             .. testoutput::
 
                 q0: ───X─────
diff --git a/src/qibo/gates/measurements.py b/src/qibo/gates/measurements.py
index 64a7a98e50..5e50ecf1c6 100644
--- a/src/qibo/gates/measurements.py
+++ b/src/qibo/gates/measurements.py
@@ -247,7 +247,7 @@ def on_qubits(self, qubit_map) -> "Gate":
                 c = models.Circuit(3)
                 c.add(measurement.on_qubits({0: 0, 1: 2}))
                 assert c.queue[0].result is measurement.result
-                print(c.draw())
+                c.draw()
             .. testoutput::
 
                 q0: ─M─
diff --git a/src/qibo/hamiltonians/hamiltonians.py b/src/qibo/hamiltonians/hamiltonians.py
index d8264b4b44..75fd10c7ba 100644
--- a/src/qibo/hamiltonians/hamiltonians.py
+++ b/src/qibo/hamiltonians/hamiltonians.py
@@ -564,7 +564,9 @@ def expectation_from_samples(self, freq, qubit_map=None):
             if len(term.factors) != len(set(term.factors)):
                 raise_error(NotImplementedError, "Z^k is not implemented since Z^2=I.")
         keys = list(freq.keys())
-        counts = np.array(list(freq.values())) / sum(freq.values())
+        counts = self.backend.cast(list(freq.values()), self.backend.precision) / sum(
+            freq.values()
+        )
         qubits = []
         for term in terms:
             qubits_term = []
diff --git a/src/qibo/models/circuit.py b/src/qibo/models/circuit.py
index c4586c5ae2..8bfb423abf 100644
--- a/src/qibo/models/circuit.py
+++ b/src/qibo/models/circuit.py
@@ -1,5 +1,6 @@
 import collections
 import copy
+import sys
 from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -1267,18 +1268,8 @@ def _update_draw_matrix(self, matrix, idx, gate, gate_symbol=None):
 
         return matrix, idx
 
-    def draw(self, line_wrap=70, legend=False) -> str:
-        """Draw text circuit using unicode symbols.
-
-        Args:
-            line_wrap (int): maximum number of characters per line. This option
-                split the circuit text diagram in chunks of line_wrap characters.
-            legend (bool): If ``True`` prints a legend below the circuit for
-                callbacks and channels. Default is ``False``.
-
-        Return:
-            String containing text circuit diagram.
-        """
+    def diagram(self, line_wrap: int = 70, legend: bool = False) -> str:
+        """Build the string representation of the circuit diagram."""
         # build string representation of gates
         matrix = [[] for _ in range(self.nqubits)]
         idx = [0] * self.nqubits
@@ -1369,3 +1360,21 @@ def chunkstring(string, length):
             output += table
 
         return output.rstrip("\n")
+
+    def __str__(self):
+        return self.diagram()
+
+    def draw(self, line_wrap: int = 70, legend: bool = False):
+        """Draw text circuit using unicode symbols.
+
+        Args:
+            line_wrap (int, optional): maximum number of characters per line. This option
+                split the circuit text diagram in chunks of line_wrap characters.
+                Defaults to :math:`70`.
+            legend (bool, optional): If ``True`` prints a legend below the circuit for
+                callbacks and channels. Defaults to ``False``.
+
+        Returns:
+            String containing text circuit diagram.
+        """
+        sys.stdout.write(self.diagram(line_wrap, legend) + "\n")
diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py
index ef870535ed..ccaacca39d 100644
--- a/src/qibo/models/dbi/double_bracket.py
+++ b/src/qibo/models/dbi/double_bracket.py
@@ -3,6 +3,7 @@
 from typing import Optional
 
 import numpy as np
+import optuna
 
 from qibo.config import raise_error
 from qibo.hamiltonians import Hamiltonian
@@ -55,7 +56,7 @@ class DoubleBracketScheduling(Enum):
     """Define the DBI scheduling strategies."""
 
     hyperopt = hyperopt_step
-    """Use hyperopt package."""
+    """Use optuna package to hyperoptimize the DBI step."""
     grid_search = grid_search_step
     """Use greedy grid search."""
     polynomial_approximation = polynomial_step
diff --git a/src/qibo/models/dbi/utils_dbr_strategies.py b/src/qibo/models/dbi/utils_dbr_strategies.py
index fb71cdf49f..3f7ff06221 100644
--- a/src/qibo/models/dbi/utils_dbr_strategies.py
+++ b/src/qibo/models/dbi/utils_dbr_strategies.py
@@ -1,4 +1,4 @@
-import hyperopt
+import optuna
 
 from qibo.backends import _check_backend
 from qibo.models.dbi.double_bracket import *
@@ -155,7 +155,7 @@ def gradient_descent(
     lr_max: float = 1,
     max_evals: int = 100,
     space: callable = None,
-    optimizer: callable = hyperopt.tpe,
+    optimizer: optuna.samplers.BaseSampler = optuna.samplers.TPESampler(),
     verbose: bool = False,
     backend=None,
 ):
@@ -173,61 +173,26 @@ def gradient_descent(
         normalize (bool, optional): option to normalize the diagonal operator. Defaults to False.
         lr_min (float, optional): the minimal gradient step. Defaults to 1e-5.
         lr_max (float, optional): the maximal gradient step. Defaults to 1.
-        max_evals (int, optional): maximum number of evaluations for `lr` using `hyperopt`. Defaults to 100.
-        space (callable, optional): evalutation space for `hyperopt`. Defaults to None.
-        optimizer (callable, optional): optimizer option for `hyperopt`. Defaults to `hyperopt.tpe`.
-        verbose (bool, optional): option for printing `hyperopt` process. Defaults to False.
+        max_evals (int, optional): maximum number of evaluations for `lr` using `optuna`. Defaults to 100.
+        space (callable, optional): evalutation space for `optuna`. Defaults to None.
+        optimizer (optuna.samplers.BaseSampler, optional): optimizer option for `optuna`. Defaults to `TPESampler()`.
+        verbose (bool, optional): option for printing `optuna` process. Defaults to False.
 
     Returns:
         loss_hist (list): list of history losses of `dbi_object` throughout the double bracket rotations.
         d_params_hist (list): list of history of `d` parameters after gradient descent.
         s_hist (list): list of history of optimal `s` found.
-    Example:
-        from qibo import set_backend
-        from qibo.hamiltonians import Hamiltonian
-        from qibo.models.dbi.double_bracket import *
-        from qibo.models.dbi.utils import *
-        from qibo.models.dbi.utils_dbr_strategies import gradient_descent
-        from qibo.quantum_info import random_hermitian
-
-        nqubits = 3
-        NSTEPS = 5
-        set_backend("numpy")
-        h0 = random_hermitian(2**nqubits)
-        dbi = DoubleBracketIteration(
-            Hamiltonian(nqubits, h0),
-            mode=DoubleBracketGeneratorType.single_commutator,
-            scheduling=DoubleBracketScheduling.hyperopt,
-            cost=DoubleBracketCostFunction.off_diagonal_norm,
-        )
-        initial_off_diagonal_norm = dbi.off_diagonal_norm
-        pauli_operator_dict = generate_pauli_operator_dict(
-            nqubits, parameterization_order=1
-        )
-        pauli_operators = list(pauli_operator_dict.values())
-        # let initial d be approximation of $\Delta(H)
-        d_coef_pauli = decompose_into_Pauli_basis(
-            dbi.diagonal_h_matrix, pauli_operators=pauli_operators
-        )
-        d_pauli = sum([d_coef_pauli[i] * pauli_operators[i] for i in range(nqubits)])
-        loss_hist_pauli, d_params_hist_pauli, s_hist_pauli = gradient_descent(
-            dbi,
-            NSTEPS,
-            d_coef_pauli,
-            ParameterizationTypes.pauli,
-            pauli_operator_dict=pauli_operator_dict,
-        )
     """
     backend = _check_backend(backend)
 
     nqubits = dbi_object.nqubits
-    # TODO: write tests where this condition applies
     if (
         parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None
     ):  # pragma: no cover
         pauli_operator_dict = generate_pauli_operator_dict(
             nqubits=nqubits, parameterization_order=pauli_parameterization_order
         )
+
     d = params_to_diagonal_operator(
         d_params,
         nqubits,
@@ -236,21 +201,14 @@ def gradient_descent(
         normalize=normalize,
         backend=backend,
     )
+
     loss_hist = [dbi_object.loss(0.0, d=d)]
     d_params_hist = [d_params]
     s_hist = [0]
-    # TODO: write tests where this condition applies
-    if (
-        parameterization is ParameterizationTypes.pauli and pauli_operator_dict is None
-    ):  # pragma: no cover
-        pauli_operator_dict = generate_pauli_operator_dict(
-            nqubits=nqubits,
-            parameterization_order=pauli_parameterization_order,
-            backend=backend,
-        )
-    # first step
+
     s = dbi_object.choose_step(d=d)
     dbi_object(step=s, d=d)
+
     for _ in range(iterations):
         grad = gradient_numerical(
             dbi_object,
@@ -262,8 +220,8 @@ def gradient_descent(
             backend=backend,
         )
 
-        # set up hyperopt to find optimal lr
-        def func_loss_to_lr(lr):
+        def func_loss_to_lr(trial):
+            lr = trial.suggest_loguniform("lr", lr_min, lr_max)
             d_params_eval = [d_params[j] - grad[j] * lr for j in range(len(grad))]
             d_eval = params_to_diagonal_operator(
                 d_params_eval,
@@ -275,17 +233,14 @@ def func_loss_to_lr(lr):
             )
             return dbi_object.loss(step=s, d=d_eval)
 
-        if space is None:
-            space = hyperopt.hp.loguniform("lr", np.log(lr_min), np.log(lr_max))
+        # create a study using the specified optimizer (sampler)
+        study = optuna.create_study(sampler=optimizer, direction="minimize")
 
-        best = hyperopt.fmin(
-            fn=func_loss_to_lr,
-            space=space,
-            algo=optimizer.suggest,
-            max_evals=max_evals,
-            verbose=verbose,
-        )
-        lr = best["lr"]
+        # optimize the function
+        study.optimize(func_loss_to_lr, n_trials=max_evals)
+
+        # get the best learning rate
+        lr = study.best_params["lr"]
 
         d_params = [d_params[j] - grad[j] * lr for j in range(len(grad))]
         d = params_to_diagonal_operator(
@@ -303,4 +258,5 @@ def func_loss_to_lr(lr):
         loss_hist.append(dbi_object.loss(0.0, d=d))
         d_params_hist.append(d_params)
         s_hist.append(s)
+
     return loss_hist, d_params_hist, s_hist
diff --git a/src/qibo/models/dbi/utils_scheduling.py b/src/qibo/models/dbi/utils_scheduling.py
index 130cd88f30..1f08ca2e04 100644
--- a/src/qibo/models/dbi/utils_scheduling.py
+++ b/src/qibo/models/dbi/utils_scheduling.py
@@ -1,9 +1,8 @@
 import math
-from functools import partial
 from typing import Optional
 
-import hyperopt
 import numpy as np
+import optuna
 
 error = 1e-3
 
@@ -41,47 +40,45 @@ def grid_search_step(
 
 
 def hyperopt_step(
-    dbi_object,
+    self,
     step_min: float = 1e-5,
     step_max: float = 1,
-    max_evals: int = 100,
-    space: callable = None,
-    optimizer: callable = None,
+    max_evals: int = 1000,
     look_ahead: int = 1,
-    d: Optional[np.array] = None,
+    verbose: bool = False,
+    d: np.array = None,
+    optimizer: optuna.samplers.BaseSampler = None,
 ):
     """
-    Optimize iteration step using hyperopt.
+    Optimize iteration step using Optuna.
 
     Args:
         step_min: lower bound of the search grid;
         step_max: upper bound of the search grid;
-        max_evals: maximum number of iterations done by the hyperoptimizer;
-        space: see hyperopt.hp possibilities;
-        optimizer: see hyperopt algorithms;
+        max_evals: maximum number of trials done by the optimizer;
         look_ahead: number of iteration steps to compute the loss function;
-        d: diagonal operator for generating double-bracket iterations.
+        verbose: level of verbosity;
+        d: diagonal operator for generating double-bracket iterations;
+        optimizer: Optuna sampler for the search algorithm (e.g.,
+            optuna.samplers.TPESampler()).
+            See: https://optuna.readthedocs.io/en/stable/reference/samplers/index.html
 
     Returns:
-        (float): optimized best iteration step (minimizing loss function).
+        (float): optimized best iteration step.
     """
-    if space is None:
-        space = hyperopt.hp.uniform
+    optuna.logging.set_verbosity(optuna.logging.WARNING)
+
+    def objective(trial):
+        step = trial.suggest_float("step", step_min, step_max)
+        return self.loss(step, d=d, look_ahead=look_ahead)
+
     if optimizer is None:
-        optimizer = hyperopt.tpe
-    if d is None:
-        d = dbi_object.diagonal_h_matrix
+        optimizer = optuna.samplers.TPESampler()
 
-    space = space("step", step_min, step_max)
+    study = optuna.create_study(direction="minimize", sampler=optimizer)
+    study.optimize(objective, n_trials=max_evals, show_progress_bar=verbose)
 
-    best = hyperopt.fmin(
-        fn=partial(dbi_object.loss, d=d, look_ahead=look_ahead),
-        space=space,
-        algo=optimizer.suggest,
-        max_evals=max_evals,
-        show_progressbar=False,
-    )
-    return best["step"]
+    return study.best_params["step"]
 
 
 def polynomial_step(
diff --git a/src/qibo/models/error_mitigation.py b/src/qibo/models/error_mitigation.py
index 3b16f8ac9d..e85b7cfd73 100644
--- a/src/qibo/models/error_mitigation.py
+++ b/src/qibo/models/error_mitigation.py
@@ -1,6 +1,7 @@
 """Error Mitigation Methods."""
 
 import math
+from inspect import signature
 from itertools import product
 
 import numpy as np
@@ -207,9 +208,12 @@ def ZNE(
         )
         expected_values.append(val)
 
-    gamma = get_gammas(noise_levels, analytical=solve_for_gammas)
+    gamma = backend.cast(
+        get_gammas(noise_levels, analytical=solve_for_gammas), backend.precision
+    )
+    expected_values = backend.cast(expected_values, backend.precision)
 
-    return np.sum(gamma * expected_values)
+    return backend.np.sum(gamma * expected_values)
 
 
 def sample_training_circuit_cdr(
@@ -302,6 +306,47 @@ def sample_training_circuit_cdr(
     return sampled_circuit
 
 
+def _curve_fit(
+    backend, model, params, xdata, ydata, lr=1.0, max_iter=int(1e2), tolerance_grad=1e-5
+):
+    """
+    Fits a model with given parameters on the data points (x,y). This is generally based on the
+    `scipy.optimize.curve_fit` function, except for the `PyTorchBackend` which makes use of the
+    `torch.optim.LBFGS` optimizer.
+
+    Args:
+    backend (:class:`qibo.backends.Backend`): simulation engine, this is only useful for `pytorch`.
+    model (function): model to fit, it should be a callable ``model(x, *params)``.
+    params (ndarray): initial parameters of the model.
+    xdata (ndarray): x data, i.e. inputs to the model.
+    ydata (ndarray): y data, i.e. targets ``y = model(x, *params)``.
+    lr (float, optional): learning rate, defaults to ``1``. Used only in the `pytorch` case.
+    max_iter (int, optional): maximum number of iterations, defaults to ``100``. Used only in the `pytorch` case.
+    tolerance_grad (float, optional): gradient tolerance, optimization stops after reaching it, defaults to ``1e-5``. Used only in the `pytorch` case.
+
+    Returns:
+        ndarray: the optimal parameters.
+    """
+    if backend.name == "pytorch":
+        # pytorch has some problems with the `scipy.optim.curve_fit` function
+        # thus we use a `torch.optim` optimizer
+        loss = lambda pred, target: backend.np.mean((pred - target) ** 2)
+        optimizer = backend.np.optim.LBFGS(
+            [params], lr=lr, max_iter=max_iter, tolerance_grad=tolerance_grad
+        )
+
+        def closure():
+            optimizer.zero_grad()
+            output = model(xdata, *params)
+            loss_val = loss(output, ydata)
+            loss_val.backward(retain_graph=True)
+            return loss_val
+
+        optimizer.step(closure)
+        return params
+    return curve_fit(model, xdata, ydata, p0=params)[0]
+
+
 def CDR(
     circuit,
     observable,
@@ -382,7 +427,17 @@ def CDR(
         )
         train_val["noisy"].append(val)
 
-    optimal_params = curve_fit(model, train_val["noisy"], train_val["noise-free"])[0]
+    nparams = (
+        len(signature(model).parameters) - 1
+    )  # first arg is the input and the *params afterwards
+    params = backend.cast(local_state.random(nparams), backend.precision)
+    optimal_params = _curve_fit(
+        backend,
+        model,
+        params,
+        backend.cast(train_val["noisy"], backend.precision),
+        backend.cast(train_val["noise-free"], backend.precision),
+    )
 
     val = get_expectation_val_with_readout_mitigation(
         circuit,
@@ -408,7 +463,7 @@ def vnCDR(
     noise_levels,
     noise_model,
     nshots: int = 10000,
-    model=lambda x, *params: (x * np.array(params).reshape(-1, 1)).sum(0),
+    model=None,
     n_training_samples: int = 100,
     insertion_gate: str = "CNOT",
     full_output: bool = False,
@@ -463,6 +518,9 @@ def vnCDR(
     """
     backend, local_state = _check_backend_and_local_state(seed, backend)
 
+    if model is None:
+        model = lambda x, *params: backend.np.sum(x * backend.np.vstack(params), axis=0)
+
     if readout is None:
         readout = {}
 
@@ -475,7 +533,7 @@ def vnCDR(
     for circ in training_circuits:
         result = backend.execute_circuit(circ, nshots=nshots)
         val = result.expectation_from_samples(observable)
-        train_val["noise-free"].append(val)
+        train_val["noise-free"].append(float(val))
         for level in noise_levels:
             noisy_c = get_noisy_circuit(circ, level, insertion_gate=insertion_gate)
             val = get_expectation_val_with_readout_mitigation(
@@ -488,12 +546,21 @@ def vnCDR(
                 seed=local_state,
                 backend=backend,
             )
-            train_val["noisy"].append(val)
-
-    noisy_array = np.array(train_val["noisy"]).reshape(-1, len(noise_levels))
+            train_val["noisy"].append(float(val))
 
-    params = local_state.random(len(noise_levels))
-    optimal_params = curve_fit(model, noisy_array.T, train_val["noise-free"], p0=params)
+    noisy_array = backend.cast(train_val["noisy"], backend.precision).reshape(
+        -1, len(noise_levels)
+    )
+    params = backend.cast(local_state.random(len(noise_levels)), backend.precision)
+    optimal_params = _curve_fit(
+        backend,
+        model,
+        params,
+        noisy_array.T,
+        backend.cast(train_val["noise-free"], backend.precision),
+        lr=1,
+        tolerance_grad=1e-7,
+    )
 
     val = []
     for level in noise_levels:
@@ -510,7 +577,10 @@ def vnCDR(
         )
         val.append(expval)
 
-    mit_val = model(np.array(val).reshape(-1, 1), *optimal_params[0])[0]
+    mit_val = model(
+        backend.cast(val, backend.precision).reshape(-1, 1),
+        *optimal_params,
+    )[0]
 
     if full_output:
         return mit_val, val, optimal_params, train_val
@@ -789,8 +859,7 @@ def get_expectation_val_with_readout_mitigation(
     exp_val = circuit_result.expectation_from_samples(observable)
 
     if "ncircuits" in readout:
-        exp_val /= circuit_result_cal.expectation_from_samples(observable)
-
+        return exp_val / circuit_result_cal.expectation_from_samples(observable)
     return exp_val
 
 
@@ -1038,8 +1107,9 @@ def ICS(
         data["noisy"].append(noisy_expectation)
         lambda_list.append(1 - noisy_expectation / expectation)
 
-    dep_param = np.mean(lambda_list)
-    dep_param_std = np.std(lambda_list)
+    lambda_list = backend.cast(lambda_list, backend.precision)
+    dep_param = backend.np.mean(lambda_list)
+    dep_param_std = backend.np.std(lambda_list)
 
     noisy_expectation = get_expectation_val_with_readout_mitigation(
         circuit,
diff --git a/src/qibo/quantum_info/clifford.py b/src/qibo/quantum_info/clifford.py
index f4ec2d6bce..e8decd7444 100644
--- a/src/qibo/quantum_info/clifford.py
+++ b/src/qibo/quantum_info/clifford.py
@@ -61,7 +61,10 @@ def __post_init__(self):
             self.symplectic_matrix = self.data
             if self.symplectic_matrix.shape[0] % 2 == 0:
                 self.symplectic_matrix = np.vstack(
-                    (self.symplectic_matrix, np.zeros(self.symplectic_matrix.shape[1]))
+                    (
+                        self.symplectic_matrix,
+                        np.zeros(self.symplectic_matrix.shape[1], dtype=np.uint8),
+                    )
                 )
             self.nqubits = int((self.symplectic_matrix.shape[1] - 1) / 2)
         if self._backend is None:
diff --git a/src/qibo/quantum_info/entropies.py b/src/qibo/quantum_info/entropies.py
index c9c889b603..31972c4ff5 100644
--- a/src/qibo/quantum_info/entropies.py
+++ b/src/qibo/quantum_info/entropies.py
@@ -3,13 +3,11 @@
 from typing import Union
 
 import numpy as np
-from scipy.linalg import fractional_matrix_power
 
 from qibo.backends import _check_backend
-from qibo.backends.pytorch import PyTorchBackend
 from qibo.config import PRECISION_TOL, raise_error
-from qibo.quantum_info.linalg_operations import partial_trace
-from qibo.quantum_info.metrics import _check_hermitian_or_not_gpu, purity
+from qibo.quantum_info.linalg_operations import matrix_power, partial_trace
+from qibo.quantum_info.metrics import _check_hermitian, purity
 
 
 def shannon_entropy(prob_dist, base: float = 2, backend=None):
@@ -30,7 +28,7 @@ def shannon_entropy(prob_dist, base: float = 2, backend=None):
             Defaults to ``None``.
 
     Returns:
-        (float): Shannon entropy :math:`H(\\mathcal{p})`.
+        float: Shannon entropy :math:`H(\\mathcal{p})`.
     """
     backend = _check_backend(backend)
 
@@ -143,6 +141,40 @@ def classical_relative_entropy(prob_dist_p, prob_dist_q, base: float = 2, backen
     return entropy_p - relative
 
 
+def classical_mutual_information(
+    prob_dist_joint, prob_dist_p, prob_dist_q, base: float = 2, backend=None
+):
+    """Calculates the classical mutual information of two random variables.
+
+    Given two random variables :math:`(X, \\, Y)`, their mutual information is given by
+
+    .. math::
+        I(X, \\, Y) \\equiv H(p(x)) + H(q(y)) - H(p(x, \\, y)) \\, ,
+
+    where :math:`p(x, \\, y)` is the joint probability distribution of :math:`(X, Y)`,
+    :math:`p(x)` is the marginal probability distribution of :math:`X`,
+    :math:`q(y)` is the marginal probability distribution of :math:`Y`,
+    and :math:`H(\\cdot)` is the :func:`qibo.quantum_info.entropies.shannon_entropy`.
+
+    Args:
+        prob_dist_joint (ndarray): joint probability distribution :math:`p(x, \\, y)`.
+        prob_dist_p (ndarray): marginal probability distribution :math:`p(x)`.
+        prob_dist_q (ndarray): marginal probability distribution :math:`q(y)`.
+        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: Mutual information :math:`I(X, \\, Y)`.
+    """
+    return (
+        shannon_entropy(prob_dist_p, base, backend)
+        + shannon_entropy(prob_dist_q, base, backend)
+        - shannon_entropy(prob_dist_joint, base, backend)
+    )
+
+
 def classical_renyi_entropy(
     prob_dist, alpha: Union[float, int], base: float = 2, backend=None
 ):
@@ -458,9 +490,7 @@ def von_neumann_entropy(
 
     eigenvalues = backend.calculate_eigenvalues(
         state,
-        hermitian=(
-            not check_hermitian or _check_hermitian_or_not_gpu(state, backend=backend)
-        ),
+        hermitian=(not check_hermitian or _check_hermitian(state, backend=backend)),
     )
 
     log_prob = backend.np.where(
@@ -549,15 +579,11 @@ def relative_von_neumann_entropy(
 
     eigenvalues_state = backend.calculate_eigenvalues(
         state,
-        hermitian=(
-            not check_hermitian or _check_hermitian_or_not_gpu(state, backend=backend)
-        ),
+        hermitian=(not check_hermitian or _check_hermitian(state, backend=backend)),
     )
     eigenvalues_target = backend.calculate_eigenvalues(
         target,
-        hermitian=(
-            not check_hermitian or _check_hermitian_or_not_gpu(target, backend=backend)
-        ),
+        hermitian=(not check_hermitian or _check_hermitian(target, backend=backend)),
     )
 
     log_state = backend.np.where(
@@ -580,6 +606,50 @@ def relative_von_neumann_entropy(
     return float(backend.np.real(entropy_state - relative))
 
 
+def mutual_information(
+    state, partition, base: float = 2, check_hermitian: bool = False, backend=None
+):
+    """Calculates the mutual information of a bipartite state.
+
+    Given a qubit ``partition`` :math:`A`, the mutual information
+    of state :math:`\\rho` is given by
+
+    .. math::
+        I(\\rho}) \\equiv S(\\rho_{A}) + S(\\rho_{B}) - S(\\rho) \\, ,
+
+    where :math:`B` is the remaining qubits that are not in partition :math:`A`,
+    and :math:`S(\\cdot)` is the :func:`qibo.quantum_info.von_neumann_entropy`.
+
+    Args:
+        state (ndarray): statevector or density matrix.
+        partition (Union[List[int], Tuple[int]]): indices of qubits in partition :math:`A`.
+        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: Mutual information :math:`I(\\rho)` of ``state`` :math:`\\rho`.
+    """
+    nqubits = np.log2(len(state))
+
+    if not nqubits.is_integer():
+        raise_error(ValueError, f"dimensions of ``state`` must be a power of 2.")
+
+    partition_b = set(list(range(int(nqubits)))) ^ set(list(partition))
+
+    state_a = partial_trace(state, partition_b, backend)
+    state_b = partial_trace(state, partition, backend)
+
+    return (
+        von_neumann_entropy(state_a, base, check_hermitian, False, backend)
+        + von_neumann_entropy(state_b, base, check_hermitian, False, backend)
+        - von_neumann_entropy(state, base, check_hermitian, False, backend)
+    )
+
+
 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`.
 
@@ -652,7 +722,7 @@ def renyi_entropy(state, alpha: Union[float, int], base: float = 2, backend=None
             / np.log2(base)
         )
 
-    log = backend.np.log2(backend.np.trace(_matrix_power(state, alpha, backend)))
+    log = backend.np.log2(backend.np.trace(matrix_power(state, alpha, backend)))
 
     return (1 / (1 - alpha)) * log / np.log2(base)
 
@@ -751,8 +821,8 @@ def relative_renyi_entropy(
         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)
+        new_state = matrix_power(state, 0.5, backend)
+        new_target = matrix_power(target, 0.5, backend)
 
         log = backend.np.log2(
             backend.calculate_norm_density_matrix(new_state @ new_target, order=1)
@@ -760,8 +830,8 @@ def relative_renyi_entropy(
 
         return -2 * log / np.log2(base)
 
-    log = _matrix_power(state, alpha, backend)
-    log = log @ _matrix_power(target, 1 - alpha, backend)
+    log = matrix_power(state, alpha, backend)
+    log = log @ matrix_power(target, 1 - alpha, backend)
     log = backend.np.log2(backend.np.trace(log))
 
     return (1 / (alpha - 1)) * log / np.log2(base)
@@ -819,7 +889,7 @@ def tsallis_entropy(state, alpha: float, base: float = 2, backend=None):
         return von_neumann_entropy(state, base=base, backend=backend)
 
     return (1 / (1 - alpha)) * (
-        backend.np.trace(_matrix_power(state, alpha, backend)) - 1
+        backend.np.trace(matrix_power(state, alpha, backend)) - 1
     )
 
 
@@ -881,19 +951,3 @@ def entanglement_entropy(
     )
 
     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 = backend.np.copy(matrix)
-
-    if len(new_matrix.shape) == 1:
-        new_matrix = backend.np.outer(new_matrix, backend.np.conj(new_matrix))
-
-    return backend.cast(fractional_matrix_power(backend.to_numpy(new_matrix), alpha))
diff --git a/src/qibo/quantum_info/linalg_operations.py b/src/qibo/quantum_info/linalg_operations.py
index ae87ba4e12..6fc48e6e74 100644
--- a/src/qibo/quantum_info/linalg_operations.py
+++ b/src/qibo/quantum_info/linalg_operations.py
@@ -196,3 +196,21 @@ def matrix_exponentiation(
     backend = _check_backend(backend)
 
     return backend.calculate_matrix_exp(phase, matrix, eigenvectors, eigenvalues)
+
+
+def matrix_power(matrix, power: Union[float, int], backend=None):
+    """Given a ``matrix`` :math:`A` and power :math:`\\alpha`, calculate :math:`A^{\\alpha}`.
+
+    Args:
+        matrix (ndarray): matrix whose power to calculate.
+        power (float or int): power to raise ``matrix`` to.
+        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:
+        ndarray: matrix power :math:`A^{\\alpha}`.
+    """
+    backend = _check_backend(backend)
+
+    return backend.calculate_matrix_power(matrix, power)
diff --git a/src/qibo/quantum_info/metrics.py b/src/qibo/quantum_info/metrics.py
index 4699efbda6..c6b78f46c8 100644
--- a/src/qibo/quantum_info/metrics.py
+++ b/src/qibo/quantum_info/metrics.py
@@ -234,7 +234,7 @@ def fidelity(state, target, check_hermitian: bool = False, backend=None):
             abs(purity_state - 1) > PRECISION_TOL
             and abs(purity_target - 1) > PRECISION_TOL
         ):
-            hermitian = check_hermitian is False or _check_hermitian_or_not_gpu(
+            hermitian = check_hermitian is False or _check_hermitian(
                 state, backend=backend
             )
             # using eigh since rho is supposed to be Hermitian
@@ -812,10 +812,8 @@ def frame_potential(
     return potential / samples**2
 
 
-def _check_hermitian_or_not_gpu(matrix, backend=None):
-    """Checks if a given matrix is Hermitian and whether the backend is neither
-    :class:`qibojit.backends.CupyBackend` nor
-    :class:`qibojit.backends.CuQuantumBackend`.
+def _check_hermitian(matrix, backend=None):
+    """Checks if a given matrix is Hermitian.
 
     Args:
         matrix: input array.
@@ -825,10 +823,6 @@ def _check_hermitian_or_not_gpu(matrix, backend=None):
 
     Returns:
         bool: whether the matrix is Hermitian.
-
-    Raises:
-        NotImplementedError: If `matrix` is not Hermitian and
-        `backend` is not :class:`qibojit.backends.CupyBackend`
     """
     backend = _check_backend(backend)
 
@@ -838,14 +832,4 @@ def _check_hermitian_or_not_gpu(matrix, backend=None):
 
     hermitian = bool(float(norm) <= PRECISION_TOL)
 
-    if hermitian is False and backend.__class__.__name__ in [
-        "CupyBackend",
-        "CuQuantumBackend",
-    ]:  # pragma: no cover
-        raise_error(
-            NotImplementedError,
-            "GPU backends do not support `np.linalg.eig` "
-            + "or `np.linalg.eigvals` for non-Hermitian matrices.",
-        )
-
     return hermitian
diff --git a/src/qibo/quantum_info/utils.py b/src/qibo/quantum_info/utils.py
index ef21f2c97f..845b6ac153 100644
--- a/src/qibo/quantum_info/utils.py
+++ b/src/qibo/quantum_info/utils.py
@@ -280,7 +280,7 @@ def hellinger_fidelity(prob_dist_p, prob_dist_q, validate: bool = False, backend
             :class:`qibo.backends.GlobalBackend`. Defaults to ``None``.
 
     Returns:
-        (float): Hellinger fidelity.
+        float: Hellinger fidelity.
 
     """
     backend = _check_backend(backend)
@@ -317,7 +317,7 @@ def hellinger_shot_error(
             :class:`qibo.backends.GlobalBackend`. Defaults to ``None``.
 
     Returns:
-        (float): Hellinger fidelity error.
+        float: Hellinger fidelity error.
 
     """
     backend = _check_backend(backend)
@@ -339,6 +339,58 @@ def hellinger_shot_error(
     return hellinger_error
 
 
+def total_variation_distance(
+    prob_dist_p, prob_dist_q, validate: bool = False, backend=None
+):
+    """Calculate the total variation distance between two discrete probability distributions.
+
+    For probabilities :math:`p` and :math:`q`, the total variation distance is defined as
+
+    .. math::
+        \\operatorname{TVD}(p, \\, q) = \\frac{1}{2} \\, \\|p - q\\|_{1}
+            = \\frac{1}{2} \\, \\sum_{x} \\, \\left|p(x) - q(x)\\right| \\, ,
+
+    where :math:`\\|\\cdot\\|_{1}` detones the :math:`\\ell_{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 measure.
+    """
+    backend = _check_backend(backend)
+
+    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 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 backend.np.abs(backend.np.sum(prob_dist_p) - 1.0) > PRECISION_TOL:
+            raise_error(ValueError, "First probability array must sum to 1.")
+
+        if backend.np.abs(backend.np.sum(prob_dist_q) - 1.0) > PRECISION_TOL:
+            raise_error(ValueError, "Second probability array must sum to 1.")
+
+    tvd = backend.calculate_norm(prob_dist_p - prob_dist_q, order=1)
+
+    return tvd / 2
+
+
 def haar_integral(
     nqubits: int,
     power_t: int,
diff --git a/src/qibo/ui/mpldrawer.py b/src/qibo/ui/mpldrawer.py
index 439541d38d..de8df49900 100644
--- a/src/qibo/ui/mpldrawer.py
+++ b/src/qibo/ui/mpldrawer.py
@@ -599,12 +599,10 @@ def _process_gates(array_gates, nqubits):
                     item += ("q_" + str(qbit),)
 
             for qbit in gate._control_qubits:
-                item_add = (
-                    ("q_" + str(qbit[0]),)
-                    if isinstance(qbit, tuple)
-                    else ("q_" + str(qbit),)
-                )
-                item += item_add
+                if type(qbit) is tuple:
+                    item += ("q_" + str(qbit[0]),)
+                else:
+                    item += ("q_" + str(qbit),)
 
             gates_plot.append(item)
 
diff --git a/src/qibo/ui/symbols.json b/src/qibo/ui/symbols.json
index 8d455bfcbb..7b0d368624 100644
--- a/src/qibo/ui/symbols.json
+++ b/src/qibo/ui/symbols.json
@@ -1,6 +1,5 @@
 {
     "NOP": "",
-    "CPHASE": "Z",
     "ID": "I",
     "CX": "X",
     "CZ": "Z",
diff --git a/tests/test_hamiltonians_symbolic.py b/tests/test_hamiltonians_symbolic.py
index 9ddc545312..54d8dac2e3 100644
--- a/tests/test_hamiltonians_symbolic.py
+++ b/tests/test_hamiltonians_symbolic.py
@@ -395,4 +395,4 @@ def test_symbolic_hamiltonian_with_constant(backend):
     h = hamiltonians.SymbolicHamiltonian(1e6 - Z(0), backend=backend)
 
     result = c.execute(nshots=10000)
-    assert result.expectation_from_samples(h) == approx(1e6, rel=1e-5, abs=0.0)
+    assert float(result.expectation_from_samples(h)) == approx(1e6, rel=1e-5, abs=0.0)
diff --git a/tests/test_measurements.py b/tests/test_measurements.py
index 979cb843b6..34d8851dc8 100644
--- a/tests/test_measurements.py
+++ b/tests/test_measurements.py
@@ -6,8 +6,9 @@
 import numpy as np
 import pytest
 
-from qibo import gates, models
+from qibo import Circuit, gates
 from qibo.measurements import MeasurementResult
+from qibo.models import QFT
 
 
 def assert_result(
@@ -62,7 +63,7 @@ def assert_register_result(
 @pytest.mark.parametrize("n", [0, 1])
 @pytest.mark.parametrize("nshots", [100, 1000000])
 def test_measurement_gate(backend, n, nshots):
-    c = models.Circuit(2)
+    c = Circuit(2)
     if n:
         c.add(gates.X(1))
     c.add(gates.M(1))
@@ -78,7 +79,7 @@ def test_measurement_gate(backend, n, nshots):
 
 
 def test_multiple_qubit_measurement_gate(backend):
-    c = models.Circuit(2)
+    c = Circuit(2)
     c.add(gates.X(0))
     c.add(gates.M(0, 1))
     result = backend.execute_circuit(c, nshots=100)
@@ -105,7 +106,7 @@ def test_measurement_gate_errors(backend):
 
 
 def test_measurement_circuit(backend, accelerators):
-    c = models.Circuit(4, accelerators)
+    c = Circuit(4, accelerators)
     c.add(gates.X(0))
     c.add(gates.M(0))
     result = backend.execute_circuit(c, nshots=100)
@@ -116,7 +117,7 @@ def test_measurement_circuit(backend, accelerators):
 
 @pytest.mark.parametrize("registers", [False, True])
 def test_measurement_qubit_order_simple(backend, registers):
-    c = models.Circuit(2)
+    c = Circuit(2)
     c.add(gates.X(0))
     if registers:
         c.add(gates.M(1, 0))
@@ -134,7 +135,7 @@ def test_measurement_qubit_order_simple(backend, registers):
 
 @pytest.mark.parametrize("nshots", [100, 1000000])
 def test_measurement_qubit_order(backend, accelerators, nshots):
-    c = models.Circuit(6, accelerators)
+    c = Circuit(6, accelerators)
     c.add(gates.X(0))
     c.add(gates.X(1))
     c.add(gates.M(1, 5, 2, 0))
@@ -154,7 +155,7 @@ def test_measurement_qubit_order(backend, accelerators, nshots):
 
 
 def test_multiple_measurement_gates_circuit(backend):
-    c = models.Circuit(4)
+    c = Circuit(4)
     c.add(gates.X(1))
     c.add(gates.X(2))
     c.add(gates.M(0, 1))
@@ -170,7 +171,7 @@ def test_multiple_measurement_gates_circuit(backend):
 
 
 def test_circuit_with_unmeasured_qubits(backend, accelerators):
-    c = models.Circuit(5, accelerators)
+    c = Circuit(5, accelerators)
     c.add(gates.X(4))
     c.add(gates.X(2))
     c.add(gates.M(0, 2))
@@ -192,11 +193,11 @@ def test_circuit_with_unmeasured_qubits(backend, accelerators):
 
 
 def test_circuit_addition_with_measurements(backend):
-    c = models.Circuit(2)
+    c = Circuit(2)
     c.add(gates.X(0))
     c.add(gates.X(1))
 
-    meas_c = models.Circuit(2)
+    meas_c = Circuit(2)
     c.add(gates.M(0, 1))
 
     c += meas_c
@@ -213,12 +214,12 @@ def test_circuit_addition_with_measurements(backend):
 
 
 def test_circuit_addition_with_measurements_in_both_circuits(backend, accelerators):
-    c1 = models.Circuit(4, accelerators)
+    c1 = Circuit(4, accelerators)
     c1.add(gates.X(0))
     c1.add(gates.X(1))
     c1.add(gates.M(1, register_name="a"))
 
-    c2 = models.Circuit(4, accelerators)
+    c2 = Circuit(4, accelerators)
     c2.add(gates.X(0))
     c2.add(gates.M(0, register_name="b"))
 
@@ -232,7 +233,7 @@ def test_circuit_addition_with_measurements_in_both_circuits(backend, accelerato
 
 
 def test_circuit_copy_with_measurements(backend, accelerators):
-    c1 = models.Circuit(6, accelerators)
+    c1 = Circuit(6, accelerators)
     c1.add([gates.X(0), gates.X(1), gates.X(3)])
     c1.add(gates.M(5, 1, 3, register_name="a"))
     c1.add(gates.M(2, 0, register_name="b"))
@@ -250,7 +251,7 @@ def test_circuit_copy_with_measurements(backend, accelerators):
 
 
 def test_measurement_compiled_circuit(backend):
-    c = models.Circuit(2)
+    c = Circuit(2)
     c.add(gates.X(0))
     c.add(gates.M(0))
     c.add(gates.M(1))
@@ -274,14 +275,14 @@ def test_measurement_compiled_circuit(backend):
 
 def test_final_state(backend, accelerators):
     """Check that final state is logged correctly when using measurements."""
-    c = models.Circuit(4, accelerators)
+    c = Circuit(4, accelerators)
     c.add(gates.X(1))
     c.add(gates.X(2))
     c.add(gates.M(0, 1))
     c.add(gates.M(2))
     c.add(gates.X(3))
     result = backend.execute_circuit(c, nshots=100)
-    c = models.Circuit(4, accelerators)
+    c = Circuit(4, accelerators)
     c.add(gates.X(1))
     c.add(gates.X(2))
     c.add(gates.X(3))
@@ -300,7 +301,7 @@ def test_measurement_gate_bitflip_errors():
 
 
 def test_register_measurements(backend):
-    c = models.Circuit(3)
+    c = Circuit(3)
     c.add(gates.X(0))
     c.add(gates.X(1))
     c.add(gates.M(0, 2))
@@ -323,7 +324,7 @@ def test_register_measurements(backend):
 
 
 def test_measurement_qubit_order_multiple_registers(backend, accelerators):
-    c = models.Circuit(6, accelerators)
+    c = Circuit(6, accelerators)
     c.add(gates.X(0))
     c.add(gates.X(1))
     c.add(gates.X(3))
@@ -364,7 +365,7 @@ def test_measurement_qubit_order_multiple_registers(backend, accelerators):
 
 def test_registers_in_circuit_with_unmeasured_qubits(backend, accelerators):
     """Check that register measurements are unaffected by unmeasured qubits."""
-    c = models.Circuit(5, accelerators)
+    c = Circuit(5, accelerators)
     c.add(gates.X(1))
     c.add(gates.X(2))
     c.add(gates.M(0, 2, register_name="A"))
@@ -390,7 +391,7 @@ def test_registers_in_circuit_with_unmeasured_qubits(backend, accelerators):
 
 
 def test_measurement_density_matrix(backend):
-    c = models.Circuit(2, density_matrix=True)
+    c = Circuit(2, density_matrix=True)
     c.add(gates.X(0))
     c.add(gates.M(0, 1))
     result = backend.execute_circuit(c, nshots=100)
@@ -407,7 +408,7 @@ def test_measurement_density_matrix(backend):
 
 
 def test_measurement_result_vs_circuit_result(backend, accelerators):
-    c = models.Circuit(6, accelerators)
+    c = Circuit(6, accelerators)
     c.add([gates.X(0), gates.X(1), gates.X(3)])
     ma = c.add(gates.M(5, 1, 3, register_name="a"))
     mb = c.add(gates.M(2, 0, register_name="b"))
@@ -423,7 +424,7 @@ def test_measurement_result_vs_circuit_result(backend, accelerators):
 @pytest.mark.parametrize("nqubits", [1, 4])
 @pytest.mark.parametrize("outcome", [0, 1])
 def test_measurement_basis(backend, nqubits, outcome):
-    c = models.Circuit(nqubits)
+    c = Circuit(nqubits)
     if outcome:
         c.add(gates.X(q) for q in range(nqubits))
     c.add(gates.H(q) for q in range(nqubits))
@@ -433,7 +434,7 @@ def test_measurement_basis(backend, nqubits, outcome):
 
 
 def test_measurement_basis_list(backend):
-    c = models.Circuit(4)
+    c = Circuit(4)
     c.add(gates.H(0))
     c.add(gates.X(2))
     c.add(gates.H(2))
@@ -442,7 +443,7 @@ def test_measurement_basis_list(backend):
     result = backend.execute_circuit(c, nshots=100)
     assert result.frequencies() == {"0011": 100}
     assert (
-        c.draw()
+        str(c)
         == """q0: ─H─H───M─
 q1: ───────M─
 q2: ─X─H─H─M─
@@ -450,22 +451,20 @@ def test_measurement_basis_list(backend):
     )
 
 
-def test_measurement_basis_list_error(backend):
-    c = models.Circuit(4)
+def test_measurement_basis_list_error():
+    c = Circuit(4)
     with pytest.raises(ValueError):
         c.add(gates.M(0, 1, 2, 3, basis=[gates.X, gates.Z, gates.X]))
 
 
-def test_measurement_same_qubit_different_registers_error(backend):
-    c = models.Circuit(4)
+def test_measurement_same_qubit_different_registers_error():
+    c = Circuit(4)
     c.add(gates.M(0, 1, 3, register_name="a"))
     with pytest.raises(KeyError):
         c.add(gates.M(1, 2, 3, register_name="a"))
 
 
 def test_measurementsymbol_pickling(backend):
-    from qibo.models import QFT
-
     c = QFT(3)
     c.add(gates.M(0, 2, basis=[gates.X, gates.Z]))
     backend.execute_circuit(c).samples()
diff --git a/tests/test_models_circuit.py b/tests/test_models_circuit.py
index 33036dad22..8ffa586740 100644
--- a/tests/test_models_circuit.py
+++ b/tests/test_models_circuit.py
@@ -2,6 +2,7 @@
 
 from collections import Counter
 
+import numpy as np
 import pytest
 
 from qibo import Circuit, gates
@@ -500,8 +501,6 @@ def test_circuit_with_pauli_noise(measurements, noise_map):
 @pytest.mark.parametrize("include_not_trainable", [True, False])
 @pytest.mark.parametrize("format", ["list", "dict", "flatlist"])
 def test_get_parameters(trainable, include_not_trainable, format):
-    import numpy as np
-
     matrix = np.random.random((2, 2))
     c = Circuit(3)
     c.add(gates.RX(0, theta=0.123))
@@ -634,7 +633,7 @@ def test_circuit_draw():
     circuit.add(gates.SWAP(0, 4))
     circuit.add(gates.SWAP(1, 3))
 
-    assert circuit.draw() == ref
+    assert str(circuit) == ref
 
 
 def test_circuit_wire_names_errors():
@@ -654,10 +653,10 @@ def test_circuit_draw_wire_names():
     """Test circuit text draw."""
     ref = (
         "a    : ─H─U1─U1─U1─U1───────────────────────────x───\n"
-        "b    : ───o──|──|──|──H─U1─U1─U1────────────────|─x─\n"
-        "hello: ──────o──|──|────o──|──|──H─U1─U1────────|─|─\n"
-        "1    : ─────────o──|───────o──|────o──|──H─U1───|─x─\n"
-        "q4   : ────────────o──────────o───────o────o──H─x───"
+        + "b    : ───o──|──|──|──H─U1─U1─U1────────────────|─x─\n"
+        + "hello: ──────o──|──|────o──|──|──H─U1─U1────────|─|─\n"
+        + "1    : ─────────o──|───────o──|────o──|──H─U1───|─x─\n"
+        + "q4   : ────────────o──────────o───────o────o──H─x───"
     )
     circuit = Circuit(5, wire_names=["a", "b", "hello", "1", "q4"])
     for i1 in range(5):
@@ -667,47 +666,45 @@ def test_circuit_draw_wire_names():
     circuit.add(gates.SWAP(0, 4))
     circuit.add(gates.SWAP(1, 3))
 
-    assert circuit.draw() == ref
+    assert str(circuit) == ref
 
 
-def test_circuit_draw_line_wrap():
+def test_circuit_draw_line_wrap(capsys):
     """Test circuit text draw with line wrap."""
     ref_line_wrap_50 = (
         "q0:     ─H─U1─U1─U1─U1───────────────────────────x───I───f ...\n"
-        "q1:     ───o──|──|──|──H─U1─U1─U1────────────────|─x─I───| ...\n"
-        "q2:     ──────o──|──|────o──|──|──H─U1─U1────────|─|─────| ...\n"
-        "q3:     ─────────o──|───────o──|────o──|──H─U1───|─x───M─| ...\n"
-        "q4:     ────────────o──────────o───────o────o──H─x───────f ...\n"
-        "\n"
-        "q0: ... ─o────gf───M─\n"
-        "q1: ... ─U3───|──o─M─\n"
-        "q2: ... ────X─gf─o─M─\n"
-        "q3: ... ────o────o───\n"
-        "q4: ... ────o────X───"
+        + "q1:     ───o──|──|──|──H─U1─U1─U1────────────────|─x─I───| ...\n"
+        + "q2:     ──────o──|──|────o──|──|──H─U1─U1────────|─|─────| ...\n"
+        + "q3:     ─────────o──|───────o──|────o──|──H─U1───|─x───M─| ...\n"
+        + "q4:     ────────────o──────────o───────o────o──H─x───────f ...\n"
+        + "\n"
+        + "q0: ... ─o────gf───M─\n"
+        + "q1: ... ─U3───|──o─M─\n"
+        + "q2: ... ────X─gf─o─M─\n"
+        + "q3: ... ────o────o───\n"
+        + "q4: ... ────o────X───"
     )
 
     ref_line_wrap_30 = (
         "q0:     ─H─U1─U1─U1─U1──────────────── ...\n"
-        "q1:     ───o──|──|──|──H─U1─U1─U1───── ...\n"
-        "q2:     ──────o──|──|────o──|──|──H─U1 ...\n"
-        "q3:     ─────────o──|───────o──|────o─ ...\n"
-        "q4:     ────────────o──────────o────── ...\n"
-        "\n"
-        "q0: ... ───────────x───I───f─o────gf── ...\n"
-        "q1: ... ───────────|─x─I───|─U3───|──o ...\n"
-        "q2: ... ─U1────────|─|─────|────X─gf─o ...\n"
-        "q3: ... ─|──H─U1───|─x───M─|────o────o ...\n"
-        "q4: ... ─o────o──H─x───────f────o────X ...\n"
-        "\n"
-        "q0: ... ─M─\n"
-        "q1: ... ─M─\n"
-        "q2: ... ─M─\n"
-        "q3: ... ───\n"
-        "q4: ... ───"
+        + "q1:     ───o──|──|──|──H─U1─U1─U1───── ...\n"
+        + "q2:     ──────o──|──|────o──|──|──H─U1 ...\n"
+        + "q3:     ─────────o──|───────o──|────o─ ...\n"
+        + "q4:     ────────────o──────────o────── ...\n"
+        + "\n"
+        + "q0: ... ───────────x───I───f─o────gf── ...\n"
+        + "q1: ... ───────────|─x─I───|─U3───|──o ...\n"
+        + "q2: ... ─U1────────|─|─────|────X─gf─o ...\n"
+        + "q3: ... ─|──H─U1───|─x───M─|────o────o ...\n"
+        + "q4: ... ─o────o──H─x───────f────o────X ...\n"
+        + "\n"
+        + "q0: ... ─M─\n"
+        + "q1: ... ─M─\n"
+        + "q2: ... ─M─\n"
+        + "q3: ... ───\n"
+        + "q4: ... ───"
     )
 
-    import numpy as np
-
     circuit = Circuit(5)
     for i1 in range(5):
         circuit.add(gates.H(i1))
@@ -723,48 +720,52 @@ def test_circuit_draw_line_wrap():
     circuit.add(gates.GeneralizedfSim(0, 2, np.eye(2), 0))
     circuit.add(gates.X(4).controlled_by(1, 2, 3))
     circuit.add(gates.M(*range(3)))
-    assert circuit.draw(line_wrap=50) == ref_line_wrap_50
-    assert circuit.draw(line_wrap=30) == ref_line_wrap_30
 
+    circuit.draw(line_wrap=50)
+    out, _ = capsys.readouterr()
+    assert out.rstrip("\n") == ref_line_wrap_50
+
+    circuit.draw(line_wrap=30)
+    out, _ = capsys.readouterr()
+    assert out.rstrip("\n") == ref_line_wrap_30
 
-def test_circuit_draw_line_wrap_names():
+
+def test_circuit_draw_line_wrap_names(capsys):
     """Test circuit text draw with line wrap."""
     ref_line_wrap_50 = (
         "q0:     ─H─U1─U1─U1─U1───────────────────────────x───I───f ...\n"
-        "a :     ───o──|──|──|──H─U1─U1─U1────────────────|─x─I───| ...\n"
-        "q2:     ──────o──|──|────o──|──|──H─U1─U1────────|─|─────| ...\n"
-        "q3:     ─────────o──|───────o──|────o──|──H─U1───|─x───M─| ...\n"
-        "q4:     ────────────o──────────o───────o────o──H─x───────f ...\n"
-        "\n"
-        "q0: ... ─o────gf───M─\n"
-        "a : ... ─U3───|──o─M─\n"
-        "q2: ... ────X─gf─o─M─\n"
-        "q3: ... ────o────o───\n"
-        "q4: ... ────o────X───"
+        + "a :     ───o──|──|──|──H─U1─U1─U1────────────────|─x─I───| ...\n"
+        + "q2:     ──────o──|──|────o──|──|──H─U1─U1────────|─|─────| ...\n"
+        + "q3:     ─────────o──|───────o──|────o──|──H─U1───|─x───M─| ...\n"
+        + "q4:     ────────────o──────────o───────o────o──H─x───────f ...\n"
+        + "\n"
+        + "q0: ... ─o────gf───M─\n"
+        + "a : ... ─U3───|──o─M─\n"
+        + "q2: ... ────X─gf─o─M─\n"
+        + "q3: ... ────o────o───\n"
+        + "q4: ... ────o────X───"
     )
 
     ref_line_wrap_30 = (
         "q0:     ─H─U1─U1─U1─U1──────────────── ...\n"
-        "a :     ───o──|──|──|──H─U1─U1─U1───── ...\n"
-        "q2:     ──────o──|──|────o──|──|──H─U1 ...\n"
-        "q3:     ─────────o──|───────o──|────o─ ...\n"
-        "q4:     ────────────o──────────o────── ...\n"
-        "\n"
-        "q0: ... ───────────x───I───f─o────gf── ...\n"
-        "a : ... ───────────|─x─I───|─U3───|──o ...\n"
-        "q2: ... ─U1────────|─|─────|────X─gf─o ...\n"
-        "q3: ... ─|──H─U1───|─x───M─|────o────o ...\n"
-        "q4: ... ─o────o──H─x───────f────o────X ...\n"
-        "\n"
-        "q0: ... ─M─\n"
-        "a : ... ─M─\n"
-        "q2: ... ─M─\n"
-        "q3: ... ───\n"
-        "q4: ... ───"
+        + "a :     ───o──|──|──|──H─U1─U1─U1───── ...\n"
+        + "q2:     ──────o──|──|────o──|──|──H─U1 ...\n"
+        + "q3:     ─────────o──|───────o──|────o─ ...\n"
+        + "q4:     ────────────o──────────o────── ...\n"
+        + "\n"
+        + "q0: ... ───────────x───I───f─o────gf── ...\n"
+        + "a : ... ───────────|─x─I───|─U3───|──o ...\n"
+        + "q2: ... ─U1────────|─|─────|────X─gf─o ...\n"
+        + "q3: ... ─|──H─U1───|─x───M─|────o────o ...\n"
+        + "q4: ... ─o────o──H─x───────f────o────X ...\n"
+        + "\n"
+        + "q0: ... ─M─\n"
+        + "a : ... ─M─\n"
+        + "q2: ... ─M─\n"
+        + "q3: ... ───\n"
+        + "q4: ... ───"
     )
 
-    import numpy as np
-
     circuit = Circuit(5, wire_names={"q1": "a"})
     for i1 in range(5):
         circuit.add(gates.H(i1))
@@ -780,12 +781,18 @@ def test_circuit_draw_line_wrap_names():
     circuit.add(gates.GeneralizedfSim(0, 2, np.eye(2), 0))
     circuit.add(gates.X(4).controlled_by(1, 2, 3))
     circuit.add(gates.M(*range(3)))
-    assert circuit.draw(line_wrap=50) == ref_line_wrap_50
-    assert circuit.draw(line_wrap=30) == ref_line_wrap_30
+
+    circuit.draw(line_wrap=50)
+    out, _ = capsys.readouterr()
+    assert out.rstrip("\n") == ref_line_wrap_50
+
+    circuit.draw(line_wrap=30)
+    out, _ = capsys.readouterr()
+    assert out.rstrip("\n") == ref_line_wrap_30
 
 
 @pytest.mark.parametrize("legend", [True, False])
-def test_circuit_draw_channels(legend):
+def test_circuit_draw_channels(capsys, legend):
     """Check that channels are drawn correctly."""
 
     circuit = Circuit(2, density_matrix=True)
@@ -803,22 +810,24 @@ def test_circuit_draw_channels(legend):
     circuit.add(gates.CNOT(0, 1))
     circuit.add(gates.DepolarizingChannel((1,), 0.1))
 
-    ref = "q0: ─H─PN─o─PN─o─D─o─D─o───\n" "q1: ─H─PN─X─PN─X─D─X───X─D─"
+    ref = "q0: ─H─PN─o─PN─o─D─o─D─o───\n" + "q1: ─H─PN─X─PN─X─D─X───X─D─"
 
     if legend:
         ref += (
             "\n\n Legend for callbacks and channels: \n"
-            "| Gate                | Symbol   |\n"
-            "|---------------------+----------|\n"
-            "| DepolarizingChannel | D        |\n"
-            "| PauliNoiseChannel   | PN       |"
+            + "| Gate                | Symbol   |\n"
+            + "|---------------------+----------|\n"
+            + "| DepolarizingChannel | D        |\n"
+            + "| PauliNoiseChannel   | PN       |"
         )
 
-    assert circuit.draw(legend=legend) == ref
+    circuit.draw(legend=legend)
+    out, _ = capsys.readouterr()
+    assert out.rstrip("\n") == ref
 
 
 @pytest.mark.parametrize("legend", [True, False])
-def test_circuit_draw_callbacks(legend):
+def test_circuit_draw_callbacks(capsys, legend):
     """Check that callbacks are drawn correcly."""
     from qibo.callbacks import EntanglementEntropy
 
@@ -830,27 +839,29 @@ def test_circuit_draw_callbacks(legend):
     c.add(gates.CNOT(0, 1))
     c.add(gates.CallbackGate(entropy))
 
-    ref = "q0: ─EE─H─EE─o─EE─\n" "q1: ─EE───EE─X─EE─"
+    ref = "q0: ─EE─H─EE─o─EE─\n" + "q1: ─EE───EE─X─EE─"
 
     if legend:
         ref += (
             "\n\n Legend for callbacks and channels: \n"
-            "| Gate                | Symbol   |\n"
-            "|---------------------+----------|\n"
-            "| EntanglementEntropy | EE       |"
+            + "| Gate                | Symbol   |\n"
+            + "|---------------------+----------|\n"
+            + "| EntanglementEntropy | EE       |"
         )
 
-    assert c.draw(legend=legend) == ref
+    c.draw(legend=legend)
+    out, _ = capsys.readouterr()
+    assert out.rstrip("\n") == ref
 
 
 def test_circuit_draw_labels():
     """Test circuit text draw."""
     ref = (
         "q0: ─H─G1─G2─G3─G4───────────────────────────x───\n"
-        "q1: ───o──|──|──|──H─G2─G3─G4────────────────|─x─\n"
-        "q2: ──────o──|──|────o──|──|──H─G3─G4────────|─|─\n"
-        "q3: ─────────o──|───────o──|────o──|──H─G4───|─x─\n"
-        "q4: ────────────o──────────o───────o────o──H─x───"
+        + "q1: ───o──|──|──|──H─G2─G3─G4────────────────|─x─\n"
+        + "q2: ──────o──|──|────o──|──|──H─G3─G4────────|─|─\n"
+        + "q3: ─────────o──|───────o──|────o──|──H─G4───|─x─\n"
+        + "q4: ────────────o──────────o───────o────o──H─x───"
     )
     circuit = Circuit(5)
     for i1 in range(5):
@@ -861,17 +872,17 @@ def test_circuit_draw_labels():
             circuit.add(gate)
     circuit.add(gates.SWAP(0, 4))
     circuit.add(gates.SWAP(1, 3))
-    assert circuit.draw() == ref
+    assert str(circuit).rstrip("\n") == ref
 
 
-def test_circuit_draw_names():
+def test_circuit_draw_names(capsys):
     """Test circuit text draw."""
     ref = (
         "q0: ─H─cx─cx─cx─cx───────────────────────────x───\n"
-        "q1: ───o──|──|──|──H─cx─cx─cx────────────────|─x─\n"
-        "q2: ──────o──|──|────o──|──|──H─cx─cx────────|─|─\n"
-        "q3: ─────────o──|───────o──|────o──|──H─cx───|─x─\n"
-        "q4: ────────────o──────────o───────o────o──H─x───"
+        + "q1: ───o──|──|──|──H─cx─cx─cx────────────────|─x─\n"
+        + "q2: ──────o──|──|────o──|──|──H─cx─cx────────|─|─\n"
+        + "q3: ─────────o──|───────o──|────o──|──H─cx───|─x─\n"
+        + "q4: ────────────o──────────o───────o────o──H─x───"
     )
     circuit = Circuit(5)
     for i1 in range(5):
@@ -882,7 +893,9 @@ def test_circuit_draw_names():
             circuit.add(gate)
     circuit.add(gates.SWAP(0, 4))
     circuit.add(gates.SWAP(1, 3))
-    assert circuit.draw() == ref
+    circuit.draw()
+    out, _ = capsys.readouterr()
+    assert out.rstrip("\n") == ref
 
 
 def test_circuit_draw_error():
diff --git a/tests/test_models_circuit_fuse.py b/tests/test_models_circuit_fuse.py
index 6ba1ef729b..22e9c74f0e 100644
--- a/tests/test_models_circuit_fuse.py
+++ b/tests/test_models_circuit_fuse.py
@@ -236,4 +236,4 @@ def test_fused_gate_draw():
     circuit.add(gates.SWAP(0, 4))
     circuit.add(gates.SWAP(1, 3))
     circuit = circuit.fuse()
-    assert circuit.draw() == ref
+    assert str(circuit) == ref
diff --git a/tests/test_models_error_mitigation.py b/tests/test_models_error_mitigation.py
index 53d6166a7b..8e003993cf 100644
--- a/tests/test_models_error_mitigation.py
+++ b/tests/test_models_error_mitigation.py
@@ -317,7 +317,7 @@ def test_readout_mitigation(backend, nqubits, nmeas, method, ibu_iters):
         c, obs, noise, nshots, readout, backend=backend
     )
 
-    assert np.abs(true_val - mit_val) <= np.abs(true_val - noisy_val)
+    assert backend.np.abs(true_val - mit_val) <= backend.np.abs(true_val - noisy_val)
 
 
 @pytest.mark.parametrize("nqubits", [3])
diff --git a/tests/test_noise.py b/tests/test_noise.py
index 325ec95e2d..c7518a3a40 100644
--- a/tests/test_noise.py
+++ b/tests/test_noise.py
@@ -758,7 +758,7 @@ def test_ibmq_noise(
 
     noisy_circuit_target = noise_model_target.apply(circuit)
 
-    assert noisy_circuit.draw() == noisy_circuit_target.draw()
+    assert str(noisy_circuit) == str(noisy_circuit_target)
 
     backend.set_seed(2024)
     state = backend.execute_circuit(noisy_circuit, nshots=10)
diff --git a/tests/test_quantum_info_entropies.py b/tests/test_quantum_info_entropies.py
index 84129e3c6d..b383effed6 100644
--- a/tests/test_quantum_info_entropies.py
+++ b/tests/test_quantum_info_entropies.py
@@ -1,15 +1,15 @@
 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_mutual_information,
     classical_relative_entropy,
     classical_relative_renyi_entropy,
     classical_renyi_entropy,
     classical_tsallis_entropy,
     entanglement_entropy,
+    mutual_information,
     relative_renyi_entropy,
     relative_von_neumann_entropy,
     renyi_entropy,
@@ -17,6 +17,7 @@
     tsallis_entropy,
     von_neumann_entropy,
 )
+from qibo.quantum_info.linalg_operations import matrix_power
 from qibo.quantum_info.random_ensembles import (
     random_density_matrix,
     random_statevector,
@@ -125,6 +126,27 @@ def test_classical_relative_entropy(backend, base, kind):
     backend.assert_allclose(divergence, target, atol=1e-5)
 
 
+@pytest.mark.parametrize("base", [2, 10, np.e, 5])
+def test_classical_mutual_information(backend, base):
+    prob_p = np.random.rand(10)
+    prob_q = np.random.rand(10)
+    prob_p /= np.sum(prob_p)
+    prob_q /= np.sum(prob_q)
+
+    joint_dist = np.kron(prob_p, prob_q)
+    joint_dist /= np.sum(joint_dist)
+
+    prob_p = backend.cast(prob_p, dtype=prob_p.dtype)
+    prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+    joint_dist = backend.cast(joint_dist, dtype=joint_dist.dtype)
+
+    backend.assert_allclose(
+        classical_mutual_information(joint_dist, prob_p, prob_q, base, backend),
+        0.0,
+        atol=1e-10,
+    )
+
+
 @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])
@@ -499,6 +521,25 @@ def test_relative_entropy(backend, base, check_hermitian):
         )
 
 
+@pytest.mark.parametrize("check_hermitian", [False, True])
+@pytest.mark.parametrize("base", [2, 10, np.e, 5])
+def test_mutual_information(backend, base, check_hermitian):
+    with pytest.raises(ValueError):
+        state = np.ones((3, 3))
+        state = backend.cast(state, dtype=state.dtype)
+        test = mutual_information(state, [0], backend)
+
+    state_a = random_density_matrix(4, backend=backend)
+    state_b = random_density_matrix(4, backend=backend)
+    state = backend.np.kron(state_a, state_b)
+
+    backend.assert_allclose(
+        mutual_information(state, [0, 1], base, check_hermitian, backend),
+        0.0,
+        atol=1e-10,
+    )
+
+
 @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):
@@ -604,8 +645,18 @@ def test_relative_renyi_entropy(backend, alpha, base, state_flag, target_flag):
             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)
+                state_outer = (
+                    backend.np.outer(state, backend.np.conj(state.T))
+                    if state_flag
+                    else state
+                )
+                target_outer = (
+                    backend.np.outer(target, backend.np.conj(target.T))
+                    if target_flag
+                    else target
+                )
+                new_state = matrix_power(state_outer, 0.5, backend)
+                new_target = matrix_power(target_outer, 0.5, backend)
 
                 log = backend.np.log2(
                     backend.calculate_norm_density_matrix(
@@ -621,8 +672,8 @@ def test_relative_renyi_entropy(backend, alpha, base, state_flag, target_flag):
                 if len(target.shape) == 1:
                     target = backend.np.outer(target, backend.np.conj(target))
 
-                log = _matrix_power(state, alpha, backend)
-                log = log @ _matrix_power(target, 1 - alpha, backend)
+                log = matrix_power(state, alpha, backend)
+                log = log @ matrix_power(target, 1 - alpha, backend)
                 log = backend.np.log2(backend.np.trace(log))
 
                 log = (1 / (alpha - 1)) * log / np.log2(base)
@@ -668,7 +719,7 @@ def test_tsallis_entropy(backend, alpha, base):
         target = von_neumann_entropy(state, base=base, backend=backend)
     else:
         target = (1 / (1 - alpha)) * (
-            backend.np.trace(_matrix_power(state, alpha, backend)) - 1
+            backend.np.trace(matrix_power(state, alpha, backend)) - 1
         )
 
     backend.assert_allclose(
diff --git a/tests/test_quantum_info_operations.py b/tests/test_quantum_info_operations.py
index 057b69e6ea..3d97f1f63a 100644
--- a/tests/test_quantum_info_operations.py
+++ b/tests/test_quantum_info_operations.py
@@ -5,8 +5,10 @@
 from qibo.quantum_info.linalg_operations import (
     anticommutator,
     commutator,
+    matrix_power,
     partial_trace,
 )
+from qibo.quantum_info.metrics import purity
 from qibo.quantum_info.random_ensembles import random_density_matrix, random_statevector
 
 
@@ -109,3 +111,22 @@ def test_partial_trace(backend, density_matrix):
     Id = backend.identity_density_matrix(1, normalize=True)
 
     backend.assert_allclose(traced, Id)
+
+
+@pytest.mark.parametrize("power", [2, 2.0, "2"])
+def test_matrix_power(backend, power):
+    nqubits = 2
+    dims = 2**nqubits
+
+    state = random_density_matrix(dims, backend=backend)
+
+    if isinstance(power, str):
+        with pytest.raises(TypeError):
+            test = matrix_power(state, power, backend)
+    else:
+        power = matrix_power(state, power, backend)
+
+        backend.assert_allclose(
+            float(backend.np.real(backend.np.trace(power))),
+            purity(state, backend=backend),
+        )
diff --git a/tests/test_quantum_info_utils.py b/tests/test_quantum_info_utils.py
index 8a375e0f21..f9b8c0ff2b 100644
--- a/tests/test_quantum_info_utils.py
+++ b/tests/test_quantum_info_utils.py
@@ -17,6 +17,7 @@
     hellinger_fidelity,
     hellinger_shot_error,
     pqc_integral,
+    total_variation_distance,
 )
 
 
@@ -212,6 +213,52 @@ def test_hellinger_shot_error(backend, validate, kind):
     assert 2 * hellinger_error < hellinger_fid
 
 
+@pytest.mark.parametrize("kind", [None, list])
+@pytest.mark.parametrize("validate", [False, True])
+def test_total_variation_distance(backend, validate, kind):
+    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)
+    prob_p = backend.cast(prob_p, dtype=prob_p.dtype)
+    prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+
+    target = float(backend.calculate_norm(prob_p - prob_q, order=1) / 2)
+
+    prob_p = (
+        kind(prob_p) if kind is not None else backend.cast(prob_p, dtype=prob_p.dtype)
+    )
+    prob_q = (
+        kind(prob_q) if kind is not None else backend.cast(prob_q, dtype=prob_q.dtype)
+    )
+
+    tvd = total_variation_distance(prob_p, prob_q, validate, backend)
+    distance = hellinger_distance(prob_p, prob_q, validate, backend)
+
+    assert tvd == target
+    assert tvd <= np.sqrt(2) * distance
+    assert tvd >= distance**2
+
+
 def test_haar_integral_errors(backend):
     with pytest.raises(TypeError):
         nqubits, power_t, samples = 0.5, 2, 10
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diff --git a/tests/test_ui_mpldrawer.py b/tests/test_ui_mpldrawer.py
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+"""Tests for Qibo matplotlib drawer"""
+
+from pathlib import Path
+
+import matplotlib
+import matplotlib.pyplot
+import numpy as np
+import pytest
+
+from qibo import Circuit, callbacks, gates
+from qibo.models import QFT
+from qibo.ui.drawer_utils import FusedEndGateBarrier, FusedStartGateBarrier
+from qibo.ui.mpldrawer import (
+    _make_cluster_gates,
+    _plot_params,
+    _plot_quantum_circuit,
+    _process_gates,
+    _render_label,
+    plot_circuit,
+)
+
+matplotlib.use("agg")
+
+BASEPATH = str(Path(__file__).parent / "test_ui_array_images")
+
+
+# convert image file to numpy array
+def fig2array(fig):
+    fig.canvas.draw()
+    data = np.frombuffer(fig.canvas.buffer_rgba(), dtype=np.uint8)
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (4,))
+    return data
+
+
+# match a matplotlib figure with a image file
+def match_figure_image(fig, arr_path):
+    return np.all(fig2array(fig) == np.load(arr_path))
+
+
+@pytest.mark.parametrize("nqubits", [2, 3])
+def test_plot_circuit(nqubits):
+    """Test for main plot function"""
+    c = Circuit(nqubits)
+    c.add(gates.H(0))
+    c.add(gates.CNOT(0, 1))
+    c.add(gates.M(0))
+    c.add(gates.M(1))
+    _, fig = plot_circuit(c)
+    assert (
+        match_figure_image(
+            fig, BASEPATH + "/test_plot_circuit_" + str(nqubits) + ".npy"
+        )
+        == True
+    )
+
+
+@pytest.mark.parametrize("nqubits", [1, 2, 3])
+def test_circuit_measure(nqubits):
+    """Measure circuit"""
+    c = Circuit(nqubits)
+    c.add(gates.M(qubit) for qubit in range(nqubits - 1))
+    _, fig = plot_circuit(c)
+    assert (
+        match_figure_image(
+            fig, BASEPATH + "/test_circuit_measure_" + str(nqubits) + ".npy"
+        )
+        == True
+    )
+
+
+@pytest.mark.parametrize("nqubits", [3, 4, 5, 6])
+def test_bigger_circuit_gates(nqubits):
+    """Test for a bigger circuit"""
+    c = Circuit(nqubits)
+    c.add(gates.H(1))
+    c.add(gates.X(1))
+    c.add(gates.SX(2))
+    c.add(gates.CSX(0, 2))
+    c.add(gates.TOFFOLI(0, 1, 2))
+    c.add(gates.CNOT(1, 2))
+    c.add(gates.SWAP(1, 2))
+    c.add(gates.SiSWAP(1, 2))
+    c.add(gates.FSWAP(1, 2))
+    c.add(gates.DEUTSCH(1, 0, 2, np.pi))
+    c.add(gates.X(1))
+    c.add(gates.X(0))
+    c.add(gates.M(qubit) for qubit in range(2))
+    _, fig = plot_circuit(c)
+    assert (
+        match_figure_image(
+            fig, BASEPATH + "/test_bigger_circuit_gates_" + str(nqubits) + ".npy"
+        )
+        == True
+    )
+
+
+@pytest.mark.parametrize("clustered", [False, True])
+def test_complex_circuit(clustered):
+    """Complex circuits for several cases"""
+    c = Circuit(3)
+    c.add(gates.H(0))
+    c.add(gates.H(1))
+    c.add(gates.H(2))
+    c.add(gates.X(1))
+    c.add(gates.Z(0))
+    c.add(gates.CNOT(0, 1))
+    c.add(gates.CZ(0, 1))
+    c.add(gates.CRX(0, 1, np.pi))
+    c.add(gates.Y(1))
+    c.add(gates.RY(1, np.pi))
+    c.add(gates.CRY(1, 2, np.pi))
+    c.add(gates.Z(1))
+    c.add(gates.SX(2))
+    c.add(gates.CSX(0, 2))
+    c.add(gates.X(0))
+    c.add(gates.TOFFOLI(0, 1, 2))
+    c.add(gates.X(0))
+    c.add(gates.CNOT(1, 2))
+    c.add(gates.SWAP(1, 2))
+    c.add(gates.SWAP(1, 2).dagger())
+    c.add(gates.SX(1).dagger())
+    c.add(gates.X(0))
+    c.add(gates.X(2))
+    c.add(gates.H(0))
+    c.add(gates.SiSWAP(1, 2).dagger())
+    c.add(gates.FSWAP(1, 2).dagger())
+    c.add(gates.DEUTSCH(1, 0, 2, np.pi))
+    c.add(gates.X(0))
+    c.add(gates.M(qubit) for qubit in range(2))
+    _, fig1 = plot_circuit(c.invert(), cluster_gates=clustered, scale=0.70)
+    _, fig2 = plot_circuit(c, cluster_gates=clustered, scale=0.70)
+    assert (
+        match_figure_image(
+            fig1,
+            BASEPATH
+            + "/test_complex_circuit_fig1_"
+            + ("true" if clustered else "false")
+            + ".npy",
+        )
+        == True
+    )
+    assert (
+        match_figure_image(
+            fig2,
+            BASEPATH
+            + "/test_complex_circuit_fig2_"
+            + ("true" if clustered else "false")
+            + ".npy",
+        )
+        == True
+    )
+
+
+def test_align_gate():
+    """Test for Align gate"""
+    c = Circuit(3)
+    c.add(gates.Align(0))
+    _, fig = plot_circuit(c)
+    assert match_figure_image(fig, BASEPATH + "/test_align_gate.npy") == True
+
+
+@pytest.mark.parametrize("clustered", [False, True])
+def test_circuit_fused_gates(clustered):
+    """Test for FusedStartGateBarrier and FusedEndGateBarrier"""
+    c = QFT(5)
+    c.add(gates.M(qubit) for qubit in range(2))
+    _, fig = plot_circuit(
+        c.fuse(), scale=0.8, cluster_gates=clustered, style="quantumspain"
+    )
+    assert (
+        match_figure_image(
+            fig,
+            BASEPATH
+            + "/test_circuit_fused_gates_"
+            + ("true" if clustered else "false")
+            + ".npy",
+        )
+        == True
+    )
+
+
+def test_empty_circuit():
+    """Test for printing empty circuit"""
+    c = Circuit(2)
+    _, fig = plot_circuit(c)
+    assert match_figure_image(fig, BASEPATH + "/test_empty_circuit.npy") == True
+
+
+@pytest.mark.parametrize("clustered", [False, True])
+def test_circuit_entangled_entropy(clustered):
+    """Circuit test for printing entanglement entropy circuit"""
+    entropy = callbacks.EntanglementEntropy([0])
+    c = Circuit(2)
+    c.add(gates.CallbackGate(entropy))
+    c.add(gates.H(0))
+    c.add(gates.CallbackGate(entropy))
+    c.add(gates.CNOT(0, 1))
+    c.add(gates.CallbackGate(entropy))
+    _, fig = plot_circuit(c, scale=0.8, cluster_gates=clustered)
+    assert (
+        match_figure_image(
+            fig,
+            BASEPATH
+            + "/test_circuit_entangled_entropy_"
+            + ("true" if clustered else "false")
+            + ".npy",
+        )
+        == True
+    )
+
+
+def test_layered_circuit():
+    """Layered Circuit test"""
+    nqubits = 4
+    nlayers = 3
+
+    # Create variational ansatz circuit Twolocal
+    ansatz = Circuit(nqubits)
+    for l in range(nlayers):
+
+        ansatz.add(gates.RY(q, theta=0) for q in range(nqubits))
+
+        for i in range(nqubits - 3):
+            ansatz.add(gates.CNOT(i, i + 1))
+            ansatz.add(gates.CNOT(i, i + 2))
+            ansatz.add(gates.CNOT(i + 1, i + 2))
+            ansatz.add(gates.CNOT(i, i + 3))
+            ansatz.add(gates.CNOT(i + 1, i + 3))
+            ansatz.add(gates.CNOT(i + 2, i + 3))
+
+    ansatz.add(gates.RY(q, theta=0) for q in range(nqubits))
+    ansatz.add(gates.M(qubit) for qubit in range(2))
+    _, fig = plot_circuit(ansatz)
+    assert match_figure_image(fig, BASEPATH + "/test_layered_circuit.npy") == True
+
+
+def test_fused_gates():
+    c = Circuit(3)
+    c.add(gates.H(0))
+    c.add(gates.X(0))
+    c.add(gates.H(0))
+    c.add(gates.X(1))
+    c.add(gates.H(1))
+    _, fig = plot_circuit(c.fuse(), scale=0.8, cluster_gates=False)
+    assert match_figure_image(fig, BASEPATH + "/test_fused_gates.npy") == True
+
+
+def test_fuse_cluster():
+    """Test for clustering gates"""
+    c = Circuit(2)
+    c.add(gates.X(0))
+    c.add(gates.X(0))
+    c.add(gates.X(1))
+    c.add(gates.M(qubit) for qubit in range(2))
+    _, fig = plot_circuit(c.fuse())
+    assert match_figure_image(fig, BASEPATH + "/test_fuse_cluster.npy") == True
+
+
+def test_plot_circuit_internal():
+    """Test for circuit plotting"""
+    gates_plot = [
+        ("H", "q_0"),
+        ("U1", "q_0", "q_1"),
+        ("U1", "q_0", "q_2"),
+        ("U1", "q_0", "q_3"),
+        ("U1", "q_0", "q_4"),
+        ("H", "q_1"),
+        ("U1", "q_1", "q_2"),
+        ("U1", "q_1", "q_3"),
+        ("U1", "q_1", "q_4"),
+        ("H", "q_2"),
+        ("U1", "q_2", "q_3"),
+        ("U1", "q_2", "q_4"),
+        ("H", "q_3"),
+        ("U1", "q_3", "q_4"),
+        ("H", "q_4"),
+        ("SWAP", "q_0", "q_4"),
+        ("SWAP", "q_1", "q_3"),
+        ("MEASURE", "q_0"),
+        ("MEASURE", "q_1"),
+    ]
+
+    inits = [0, 1, 2, 3, 4]
+
+    params = {
+        "scale": 1.0,
+        "fontsize": 14.0,
+        "linewidth": 1.0,
+        "control_radius": 0.05,
+        "not_radius": 0.15,
+        "swap_delta": 0.08,
+        "label_buffer": 0.0,
+        "facecolor": "#d55e00",
+        "edgecolor": "#f0e442",
+        "fillcolor": "#cc79a7",
+        "linecolor": "#f0e442",
+        "textcolor": "#f0e442",
+        "gatecolor": "#d55e00",
+        "controlcolor": "#f0e442",
+    }
+
+    labels = ["q_0", "q_1", "q_2", "q_3", "q_4"]
+
+    ax1 = _plot_quantum_circuit(gates_plot, inits, params, labels, scale=0.7)
+    ax2 = _plot_quantum_circuit(gates_plot, inits, params, [], scale=0.7)
+    assert (
+        match_figure_image(ax1.figure, BASEPATH + "/test_plot_circuit_internal_ax1.npy")
+        == True
+    )
+    assert (
+        match_figure_image(ax2.figure, BASEPATH + "/test_plot_circuit_internal_ax2.npy")
+        == True
+    )
+
+
+def test_empty_gates():
+    "Empty gates test"
+    assert _process_gates([], 2) == []
+
+
+def test_plot_circuit_error_style():
+    """Test for style error function"""
+    style1 = _plot_params(style="test")
+    style2 = _plot_params(style="fardelejo")
+    custom_style = {
+        "facecolor": "#6497bf",
+        "edgecolor": "#01016f",
+        "linecolor": "#01016f",
+        "textcolor": "#01016f",
+        "fillcolor": "#ffb9b9",
+        "gatecolor": "#d8031c",
+        "controlcolor": "#360000",
+    }
+    style3 = _plot_params(style=custom_style)
+    assert style1["facecolor"] == "w"
+    assert style2["facecolor"] == "#e17a02"
+    assert style3["facecolor"] == "#6497bf"
+
+
+def test_fused_gates():
+    """Test for FusedStartGateBarrier and FusedEndGateBarrier"""
+    min_q = 0
+    max_q = 1
+    l_gates = 1
+    equal_qbits = True
+    start_barrier = FusedStartGateBarrier(min_q, max_q, l_gates, equal_qbits)
+    end_barrier = FusedEndGateBarrier(min_q, max_q)
+    assert start_barrier != None
+    assert end_barrier != None
+
+
+def test_render_label():
+    """Test render labels"""
+    inits = [0]
+    assert _render_label("q_0", inits) != ""
+    assert _render_label("q_8", inits) != ""
+
+
+def test_render_label_empty():
+    inits = {"q_0": None}
+    assert _render_label("q_0", inits) == ""
+
+
+def test_render_label_not_empty():
+    inits = {"q_0": r"\psi"}
+    assert _render_label("q_0", inits) != ""
+
+
+def test_cluster_gates():
+    """Test clustering gates"""
+    pgates = [
+        ("MEASURE", "q_0"),
+        ("GFF", "q_0", "q_1"),
+        ("U1", "q_0", "q_2"),
+        ("U1", "q_0", "q_3"),
+        ("U1", "q_0", "q_4"),
+        ("H", "q_1"),
+        ("U1", "q_1", "q_2"),
+        ("U1", "q_1", "q_3"),
+        ("U1", "q_1", "q_4"),
+        ("H", "q_2"),
+        ("U1", "q_2", "q_3"),
+        ("U1", "q_2", "q_4"),
+        ("H", "q_3"),
+        ("U1", "q_3", "q_4"),
+        ("H", "q_4"),
+        ("SWAP", "q_0", "q_4"),
+        ("SWAP", "q_1", "q_3"),
+        ("MEASURE", "q_0"),
+        ("MEASURE", "q_1"),
+    ]
+    assert _make_cluster_gates(pgates) != ""
+
+
+def test_target_control_qubts():
+    """Very dummy test to check the target and control qubits from gates"""
+    c = Circuit(3)
+    c.add(gates.CSX(0, 2))
+    c.queue[0]._target_qubits = ((0, 1), (0, 2))
+    c.queue[0]._control_qubits = ((0,), (0,))
+    assert _process_gates(c.queue, 3) != ""