infra: onboard to ruff

.github/workflows/python-package.yml

@@ -28,13 +28,11 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
-          pip install -e .[test]
+          pip install tox
       - name: Check code format
         run: |
           # stop the build if there are Python format errors or undefined names
-          black --check .
-          flake8
+          tox -e linters
       - name: Run unit tests
         run: |
           tox -e unit-tests

notebooks/advanced_algorithms/Quantum_Principal_Component_Analysis.ipynb

@@ -106,8 +106,8 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "X_1 = [4,3,4,4,3,3,3,3,4,4,4,5,4,3,4]\n",
-    "X_2 = [3028,1365,2726,2538,1318,1693,1412,1632,2875,3564,4412,4444,4278,3064,3857]\n",
+    "X_1 = [4, 3, 4, 4, 3, 3, 3, 3, 4, 4, 4, 5, 4, 3, 4]\n",
+    "X_2 = [3028, 1365, 2726, 2538, 1318, 1693, 1412, 1632, 2875, 3564, 4412, 4444, 4278, 3064, 3857]\n",
     "X_1 = (X_1 - np.average(X_1)) / np.std(X_1)\n",
     "X_2 = (X_2 - np.average(X_2)) / np.std(X_2)"
    ]
@@ -135,9 +135,9 @@
     }
    ],
    "source": [
-    "print('The rescaled feature vectors are')\n",
-    "print('X_1 = ', X_1)\n",
-    "print('X_2 = ', X_2)"
+    "print(\"The rescaled feature vectors are\")\n",
+    "print(\"X_1 = \", X_1)\n",
+    "print(\"X_2 = \", X_2)"
    ]
   },
   {
@@ -175,10 +175,7 @@
    "source": [
     "import pandas as pd\n",
     "\n",
-    "df = pd.DataFrame(\n",
-    "    {'X_1': X_1,\n",
-    "    'X_2': X_2}\n",
-    "    )"
+    "df = pd.DataFrame({\"X_1\": X_1, \"X_2\": X_2})"
    ]
   },
   {
@@ -248,8 +245,8 @@
    ],
    "source": [
     "sigma_eigenvalues, sigma_eigenvectors = np.linalg.eig(sigma)\n",
-    "print('sigma_eigenvalues: ', sigma_eigenvalues)\n",
-    "print('sigma_eigenvectors: ', sigma_eigenvectors)"
+    "print(\"sigma_eigenvalues: \", sigma_eigenvalues)\n",
+    "print(\"sigma_eigenvectors: \", sigma_eigenvectors)"
    ]
   },
   {
@@ -444,8 +441,8 @@
    ],
    "source": [
     "rho_eig_val, rho_eig_vec = np.linalg.eig(rho)\n",
-    "print('rho_eig_val: ', rho_eig_val)\n",
-    "print('rho_eig_vec: ', rho_eig_vec)"
+    "print(\"rho_eig_val: \", rho_eig_val)\n",
+    "print(\"rho_eig_vec: \", rho_eig_vec)"
    ]
   },
   {
@@ -610,8 +607,8 @@
     }
    ],
    "source": [
-    "tensor_product1 = np.vstack((np.flip(rho_eig_vec[1]),np.zeros(2))).ravel('F')\n",
-    "tensor_product2 = np.vstack((np.zeros(2),np.flip(rho_eig_vec[0]))).ravel('F')\n",
+    "tensor_product1 = np.vstack((np.flip(rho_eig_vec[1]), np.zeros(2))).ravel(\"F\")\n",
+    "tensor_product2 = np.vstack((np.zeros(2), np.flip(rho_eig_vec[0]))).ravel(\"F\")\n",
     "print(tensor_product1)\n",
     "print(tensor_product2)"
    ]
@@ -633,7 +630,7 @@
     }
    ],
    "source": [
-    "psi = sqrt_eig_val[0]*tensor_product1 + sqrt_eig_val[1]*tensor_product2\n",
+    "psi = sqrt_eig_val[0] * tensor_product1 + sqrt_eig_val[1] * tensor_product2\n",
     "print(psi)"
    ]
   },
@@ -685,7 +682,7 @@
     }
    ],
    "source": [
-    "rho_partial_trace = np.dot(psi.reshape((4,1)),psi.reshape((4,1)).transpose())\n",
+    "rho_partial_trace = np.dot(psi.reshape((4, 1)), psi.reshape((4, 1)).transpose())\n",
     "print(rho_partial_trace)"
    ]
   },
@@ -900,7 +897,12 @@
    "outputs": [],
    "source": [
     "def rotation_matrix(value):\n",
-    "    return np.array([[np.cos(value/2), -np.exp(1j*value)*np.sin(value/2)],[np.exp(1j*value)*np.sin(value/2), np.exp(2*1j*value)*np.cos(value/2)]])"
+    "    return np.array(\n",
+    "        [\n",
+    "            [np.cos(value / 2), -np.exp(1j * value) * np.sin(value / 2)],\n",
+    "            [np.exp(1j * value) * np.sin(value / 2), np.exp(2 * 1j * value) * np.cos(value / 2)],\n",
+    "        ]\n",
+    "    )"
    ]
   },
   {
@@ -942,59 +944,59 @@
    "source": [
     "circuit = Circuit()\n",
     "\n",
-    "for i in [1,2]:\n",
+    "for i in [1, 2]:\n",
     "    circuit.unitary(matrix=rotation_matrix(0.465), targets=[i])\n",
     "\n",
     "circuit.h(3)\n",
     "\n",
-    "for (i, j) in [[1,0], [2,4]]:\n",
+    "for i, j in [[1, 0], [2, 4]]:\n",
     "    circuit.cnot(i, j)\n",
-    "    \n",
-    "for i in [0,4]:\n",
+    "\n",
+    "for i in [0, 4]:\n",
     "    circuit.unitary(matrix=rotation_matrix(1.570), targets=[i])\n",
     "\n",
-    "for i in [1,2]:\n",
+    "for i in [1, 2]:\n",
     "    circuit.unitary(matrix=rotation_matrix(1.950), targets=[i])\n",
     "\n",
-    "for i in [1,2]:\n",
+    "for i in [1, 2]:\n",
     "    circuit.h(i)\n",
     "\n",
-    "circuit.cnot(2,1)\n",
+    "circuit.cnot(2, 1)\n",
     "\n",
     "circuit.h(2)\n",
     "\n",
-    "circuit.cnot(2,1)\n",
+    "circuit.cnot(2, 1)\n",
     "\n",
     "circuit.ti(1)\n",
     "\n",
-    "for (i, j) in [[2,1], [3,2], [2,1]]:\n",
+    "for i, j in [[2, 1], [3, 2], [2, 1]]:\n",
     "    circuit.cnot(i, j)\n",
     "\n",
     "circuit.t(1)\n",
     "\n",
-    "for (i, j) in [[3,2], [2,1]]:\n",
+    "for i, j in [[3, 2], [2, 1]]:\n",
     "    circuit.cnot(i, j)\n",
     "\n",
     "circuit.ti(1)\n",
     "\n",
-    "for (i, j) in [[2,1], [3,2], [2,1]]:\n",
+    "for i, j in [[2, 1], [3, 2], [2, 1]]:\n",
     "    circuit.cnot(i, j)\n",
     "\n",
-    "for (i,j,k) in [[1,3,2],[2,3,2]]:\n",
+    "for i, j, k in [[1, 3, 2], [2, 3, 2]]:\n",
     "    circuit.t(i)\n",
-    "    circuit.cnot(j,k)\n",
+    "    circuit.cnot(j, k)\n",
     "\n",
     "circuit.ti(2)\n",
     "circuit.t(3)\n",
     "\n",
-    "circuit.cnot(3,2)\n",
+    "circuit.cnot(3, 2)\n",
     "\n",
-    "for i in [2,3]:\n",
+    "for i in [2, 3]:\n",
     "    circuit.h(i)\n",
     "\n",
-    "circuit.cnot(2,1)\n",
+    "circuit.cnot(2, 1)\n",
     "\n",
-    "#measurement part of the quantum circuit\n",
+    "# measurement part of the quantum circuit\n",
     "circuit.expectation(Observable.Z(), target=[3])\n",
     "circuit.sample(observable=Observable.Z(), target=3)\n",
     "circuit.probability(target=3)\n",
@@ -1104,7 +1106,7 @@
    ],
    "source": [
     "v_1 = (1 + np.sqrt(2 * purity_sdk - 1)) / 2 * np.trace(sigma)\n",
-    "print('The first eigenvalue obtained by the quantum PCA using Amazon Braket SDK is: \\n', v_1)"
+    "print(\"The first eigenvalue obtained by the quantum PCA using Amazon Braket SDK is: \\n\", v_1)"
    ]
   },
   {
@@ -1136,8 +1138,8 @@
     }
    ],
    "source": [
-    "perc_v1 = abs((v_1-1.945575)/1.945575)*100\n",
-    "print('percent error first eigenvalue (%): ', perc_v1)"
+    "perc_v1 = abs((v_1 - 1.945575) / 1.945575) * 100\n",
+    "print(\"percent error first eigenvalue (%): \", perc_v1)"
    ]
   },
   {
@@ -1240,7 +1242,7 @@
    ],
    "source": [
     "a_1 = (1 + np.sqrt(2 * purity_sv1 - 1)) / 2 * np.trace(sigma)\n",
-    "print('The first eigenvalue obtained by the quantum PCA using Amazon Braket SV1 is: \\n', a_1)"
+    "print(\"The first eigenvalue obtained by the quantum PCA using Amazon Braket SV1 is: \\n\", a_1)"
    ]
   },
   {
@@ -1272,8 +1274,8 @@
     }
    ],
    "source": [
-    "perc_sv1 = abs((a_1-1.945575)/1.945575)*100\n",
-    "print('percent error first eigenvalue (%): ', perc_sv1)"
+    "perc_sv1 = abs((a_1 - 1.945575) / 1.945575) * 100\n",
+    "print(\"percent error first eigenvalue (%): \", perc_sv1)"
    ]
   },
   {

notebooks/auxiliary_functions/Random_Circuit.ipynb

@@ -67,10 +67,7 @@
     "# Code here\n",
     "local_simulator = LocalSimulator()\n",
     "gate_set = [CNot, Rx, Rz, CPhaseShift, XY]\n",
-    "circuit = random_circuit(num_qubits=5, \n",
-    "                         num_gates=30,\n",
-    "                         gate_set=gate_set,\n",
-    "                         seed=42)\n",
+    "circuit = random_circuit(num_qubits=5, num_gates=30, gate_set=gate_set, seed=42)\n",
     "task = local_simulator.run(circuit, shots=100)\n",
     "result = task.result()\n",
     "print(\"--Circuit--\")\n",

notebooks/textbook/Bells_Inequality.ipynb

@@ -166,10 +166,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# # Uncomment to run on a QPU \n",
-    "# from braket.aws import AwsDevice \n",
-    "# iqm_garnet = AwsDevice(\"arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet\") \n",
-    "# iqm_tasks = run_bell_inequality([circAB, circAC, circBC], iqm_garnet, shots=1000) \n",
+    "# # Uncomment to run on a QPU\n",
+    "# from braket.aws import AwsDevice\n",
+    "# iqm_garnet = AwsDevice(\"arn:aws:braket:eu-north-1::device/qpu/iqm/Garnet\")\n",
+    "# iqm_tasks = run_bell_inequality([circAB, circAC, circBC], iqm_garnet, shots=1000)\n",
     "# results, pAB, pAC, pBC = get_bell_inequality_results(iqm_tasks)\n"
    ]
   },
@@ -199,7 +199,9 @@
    "source": [
     "print(\"Task Summary\")\n",
     "print(f\"{tracker.quantum_tasks_statistics()} \\n\")\n",
-    "print(f\"Estimated cost to run this example: {tracker.qpu_tasks_cost() + tracker.simulator_tasks_cost():.2f} USD\")"
+    "print(\n",
+    "    f\"Estimated cost to run this example: {tracker.qpu_tasks_cost() + tracker.simulator_tasks_cost():.2f} USD\"\n",
+    ")"
    ]
   },
   {

notebooks/textbook/Bernstein_Vazirani_Algorithm.ipynb

@@ -44,10 +44,10 @@
     "from braket.experimental.algorithms.bernstein_vazirani import (\n",
     "    bernstein_vazirani_circuit,\n",
     "    get_bernstein_vazirani_results,\n",
-    "    run_bernstein_vazirani\n",
+    "    run_bernstein_vazirani,\n",
     ")\n",
     "\n",
-    "tracker = Tracker().start() # to track Braket costs"
+    "tracker = Tracker().start()  # to track Braket costs"
    ]
   },
   {
@@ -223,7 +223,9 @@
    "source": [
     "print(\"Task Summary\")\n",
     "print(f\"{tracker.quantum_tasks_statistics()} \\n\")\n",
-    "print(f\"Estimated cost to run this example: {tracker.qpu_tasks_cost() + tracker.simulator_tasks_cost():.2f} USD\")"
+    "print(\n",
+    "    f\"Estimated cost to run this example: {tracker.qpu_tasks_cost() + tracker.simulator_tasks_cost():.2f} USD\"\n",
+    ")"
    ]
   },
   {