infra: refactor pytest configuration into the pyproject file

pyproject.toml

@@ -1,2 +1,23 @@
+[tool.pytest.ini_options]
+xfail_strict = true
+# https://pytest-xdist.readthedocs.io/en/latest/known-limitations.html
+addopts = "--verbose -n logical --durations=0 --durations-min=1 --dist worksteal"
+testpaths = ["test/unit_tests",]
+# Issue #557 in `pytest-cov` (currently v4.x) has not moved for a while now,
+# but once a resolution has been adopted we can drop this "ignore".
+# Ref: https://github.com/pytest-dev/pytest-cov/issues/557
+filterwarnings = [
+  "ignore:The --rsyncdir command line argument and rsyncdirs config variable are deprecated.:DeprecationWarning",
+]
+norecursedirs = [
+  ".tox",
+  ".git",
+  "*/migrations/*",
+  "*/static/*",
+  "docs",
+  "venv",
+  "*/{{cookiecutter.project_slug}}/*",
+]
+
 [tool.black]
 line-length = 100

setup.cfg

@@ -1,18 +1,6 @@
 [aliases]
 test=pytest
 
-[tool:pytest]
-xfail_strict = true
-# https://pytest-xdist.readthedocs.io/en/latest/known-limitations.html
-addopts =
-    --verbose -n logical --durations=0 --durations-min=1 --dist worksteal
-testpaths = test/unit_tests
-filterwarnings=
-  # Issue #557 in `pytest-cov` (currently v4.x) has not moved for a while now,
-  # but once a resolution has been adopted we can drop this "ignore".
-  # Ref: https://github.com/pytest-dev/pytest-cov/issues/557
-  ignore:The --rsyncdir command line argument and rsyncdirs config variable are deprecated.:DeprecationWarning
-
 [isort]
 line_length = 100
 multi_line_output = 3

src/braket/circuits/circuit.py

@@ -613,7 +613,7 @@ def add_circuit(
         if target_mapping and target is not None:
             raise TypeError("Only one of 'target_mapping' or 'target' can be supplied.")
         elif target is not None:
-            keys = sorted(circuit.qubits)
+            keys = circuit.qubits
             values = target
             target_mapping = dict(zip(keys, values))
 

test/integ_tests/gate_model_device_testing_utils.py

@@ -287,22 +287,21 @@ def result_types_tensor_x_y_testing(device: Device, run_kwargs: dict[str, Any]):
     varphi = -0.543
     obs = Observable.X() @ Observable.Y()
     obs_targets = [0, 2]
+    expected_mean = np.sin(theta) * np.sin(phi) * np.sin(varphi)
+    expected_var = (
+        8 * np.sin(theta) ** 2 * np.cos(2 * varphi) * np.sin(phi) ** 2
+        - np.cos(2 * (theta - phi))
+        - np.cos(2 * (theta + phi))
+        + 2 * np.cos(2 * theta)
+        + 2 * np.cos(2 * phi)
+        + 14
+    ) / 16
+    expected_eigs = get_pauli_eigenvalues(1)
     circuit = get_result_types_three_qubit_circuit(theta, phi, varphi, obs, obs_targets, shots)
     tasks = (circuit, circuit.to_ir(ir_type=IRType.OPENQASM))
     for task in tasks:
         result = device.run(task, **run_kwargs).result()
 
-        expected_mean = np.sin(theta) * np.sin(phi) * np.sin(varphi)
-        expected_var = (
-            8 * np.sin(theta) ** 2 * np.cos(2 * varphi) * np.sin(phi) ** 2
-            - np.cos(2 * (theta - phi))
-            - np.cos(2 * (theta + phi))
-            + 2 * np.cos(2 * theta)
-            + 2 * np.cos(2 * phi)
-            + 14
-        ) / 16
-        expected_eigs = get_pauli_eigenvalues(1)
-
         assert_variance_expectation_sample_result(
             result, shots, expected_var, expected_mean, expected_eigs
         )
@@ -315,15 +314,15 @@ def result_types_tensor_z_z_testing(device: Device, run_kwargs: dict[str, Any]):
     varphi = -0.543
     obs = Observable.Z() @ Observable.Z()
     obs_targets = [0, 2]
+    expected_mean = 0.849694136476246
+    expected_var = 0.27801987443788634
+    expected_eigs = get_pauli_eigenvalues(1)
+
     circuit = get_result_types_three_qubit_circuit(theta, phi, varphi, obs, obs_targets, shots)
     tasks = (circuit, circuit.to_ir(ir_type=IRType.OPENQASM))
     for task in tasks:
         result = device.run(task, **run_kwargs).result()
 
-        expected_mean = 0.849694136476246
-        expected_var = 0.27801987443788634
-        expected_eigs = get_pauli_eigenvalues(1)
-
         assert_variance_expectation_sample_result(
             result, shots, expected_var, expected_mean, expected_eigs
         )
@@ -345,15 +344,15 @@ def result_types_tensor_hermitian_hermitian_testing(device: Device, run_kwargs:
     )
     obs = Observable.Hermitian(matrix1) @ Observable.Hermitian(matrix2)
     obs_targets = [0, 1, 2]
+    expected_mean = -4.30215023196904
+    expected_var = 370.71292282796804
+    expected_eigs = np.array([-70.90875406, -31.04969387, 0, 3.26468993, 38.693758])
+
     circuit = get_result_types_three_qubit_circuit(theta, phi, varphi, obs, obs_targets, shots)
     tasks = (circuit, circuit.to_ir(ir_type=IRType.OPENQASM))
     for task in tasks:
         result = device.run(task, **run_kwargs).result()
 
-        expected_mean = -4.30215023196904
-        expected_var = 370.71292282796804
-        expected_eigs = np.array([-70.90875406, -31.04969387, 0, 3.26468993, 38.693758])
-
         assert_variance_expectation_sample_result(
             result, shots, expected_var, expected_mean, expected_eigs
         )
@@ -366,21 +365,20 @@ def result_types_tensor_z_h_y_testing(device: Device, run_kwargs: dict[str, Any]
     varphi = -0.543
     obs = Observable.Z() @ Observable.H() @ Observable.Y()
     obs_targets = [0, 1, 2]
+    expected_mean = -(np.cos(varphi) * np.sin(phi) + np.sin(varphi) * np.cos(theta)) / np.sqrt(2)
+    expected_var = (
+        3
+        + np.cos(2 * phi) * np.cos(varphi) ** 2
+        - np.cos(2 * theta) * np.sin(varphi) ** 2
+        - 2 * np.cos(theta) * np.sin(phi) * np.sin(2 * varphi)
+    ) / 4
+    expected_eigs = get_pauli_eigenvalues(1)
+
     circuit = get_result_types_three_qubit_circuit(theta, phi, varphi, obs, obs_targets, shots)
     tasks = (circuit, circuit.to_ir(ir_type=IRType.OPENQASM))
     for task in tasks:
         result = device.run(task, **run_kwargs).result()
 
-        expected_mean = -(np.cos(varphi) * np.sin(phi) + np.sin(varphi) * np.cos(theta)) / np.sqrt(
-            2
-        )
-        expected_var = (
-            3
-            + np.cos(2 * phi) * np.cos(varphi) ** 2
-            - np.cos(2 * theta) * np.sin(varphi) ** 2
-            - 2 * np.cos(theta) * np.sin(phi) * np.sin(2 * varphi)
-        ) / 4
-        expected_eigs = get_pauli_eigenvalues(1)
         assert_variance_expectation_sample_result(
             result, shots, expected_var, expected_mean, expected_eigs
         )
@@ -401,50 +399,51 @@ def result_types_tensor_z_hermitian_testing(device: Device, run_kwargs: dict[str
     )
     obs = Observable.Z() @ Observable.Hermitian(array)
     obs_targets = [0, 1, 2]
+    expected_mean = 0.5 * (
+        -6 * np.cos(theta) * (np.cos(varphi) + 1)
+        - 2 * np.sin(varphi) * (np.cos(theta) + np.sin(phi) - 2 * np.cos(phi))
+        + 3 * np.cos(varphi) * np.sin(phi)
+        + np.sin(phi)
+    )
+    expected_var = (
+        1057
+        - np.cos(2 * phi)
+        + 12 * (27 + np.cos(2 * phi)) * np.cos(varphi)
+        - 2 * np.cos(2 * varphi) * np.sin(phi) * (16 * np.cos(phi) + 21 * np.sin(phi))
+        + 16 * np.sin(2 * phi)
+        - 8 * (-17 + np.cos(2 * phi) + 2 * np.sin(2 * phi)) * np.sin(varphi)
+        - 8 * np.cos(2 * theta) * (3 + 3 * np.cos(varphi) + np.sin(varphi)) ** 2
+        - 24 * np.cos(phi) * (np.cos(phi) + 2 * np.sin(phi)) * np.sin(2 * varphi)
+        - 8
+        * np.cos(theta)
+        * (
+            4
+            * np.cos(phi)
+            * (
+                4
+                + 8 * np.cos(varphi)
+                + np.cos(2 * varphi)
+                - (1 + 6 * np.cos(varphi)) * np.sin(varphi)
+            )
+            + np.sin(phi)
+            * (
+                15
+                + 8 * np.cos(varphi)
+                - 11 * np.cos(2 * varphi)
+                + 42 * np.sin(varphi)
+                + 3 * np.sin(2 * varphi)
+            )
+        )
+    ) / 16
+
+    z_array = np.diag([1, -1])
+    expected_eigs = np.linalg.eigvalsh(np.kron(z_array, array))
+
     circuit = get_result_types_three_qubit_circuit(theta, phi, varphi, obs, obs_targets, shots)
     tasks = (circuit, circuit.to_ir(ir_type=IRType.OPENQASM))
     for task in tasks:
         result = device.run(task, **run_kwargs).result()
 
-        expected_mean = 0.5 * (
-            -6 * np.cos(theta) * (np.cos(varphi) + 1)
-            - 2 * np.sin(varphi) * (np.cos(theta) + np.sin(phi) - 2 * np.cos(phi))
-            + 3 * np.cos(varphi) * np.sin(phi)
-            + np.sin(phi)
-        )
-        expected_var = (
-            1057
-            - np.cos(2 * phi)
-            + 12 * (27 + np.cos(2 * phi)) * np.cos(varphi)
-            - 2 * np.cos(2 * varphi) * np.sin(phi) * (16 * np.cos(phi) + 21 * np.sin(phi))
-            + 16 * np.sin(2 * phi)
-            - 8 * (-17 + np.cos(2 * phi) + 2 * np.sin(2 * phi)) * np.sin(varphi)
-            - 8 * np.cos(2 * theta) * (3 + 3 * np.cos(varphi) + np.sin(varphi)) ** 2
-            - 24 * np.cos(phi) * (np.cos(phi) + 2 * np.sin(phi)) * np.sin(2 * varphi)
-            - 8
-            * np.cos(theta)
-            * (
-                4
-                * np.cos(phi)
-                * (
-                    4
-                    + 8 * np.cos(varphi)
-                    + np.cos(2 * varphi)
-                    - (1 + 6 * np.cos(varphi)) * np.sin(varphi)
-                )
-                + np.sin(phi)
-                * (
-                    15
-                    + 8 * np.cos(varphi)
-                    - 11 * np.cos(2 * varphi)
-                    + 42 * np.sin(varphi)
-                    + 3 * np.sin(2 * varphi)
-                )
-            )
-        ) / 16
-
-        z_array = np.diag([1, -1])
-        expected_eigs = np.linalg.eigvalsh(np.kron(z_array, array))
         assert_variance_expectation_sample_result(
             result, shots, expected_var, expected_mean, expected_eigs
         )
@@ -465,15 +464,16 @@ def result_types_tensor_y_hermitian_testing(device: Device, run_kwargs: dict[str
     )
     obs = Observable.Y() @ Observable.Hermitian(array)
     obs_targets = [0, 1, 2]
+    expected_mean = 1.4499810303182408
+    expected_var = 74.03174647518193
+    y_array = np.array([[0, -1j], [1j, 0]])
+    expected_eigs = np.linalg.eigvalsh(np.kron(y_array, array))
+
     circuit = get_result_types_three_qubit_circuit(theta, phi, varphi, obs, obs_targets, shots)
     tasks = (circuit, circuit.to_ir(ir_type=IRType.OPENQASM))
     for task in tasks:
         result = device.run(task, **run_kwargs).result()
 
-        expected_mean = 1.4499810303182408
-        expected_var = 74.03174647518193
-        y_array = np.array([[0, -1j], [1j, 0]])
-        expected_eigs = np.linalg.eigvalsh(np.kron(y_array, array))
         assert_variance_expectation_sample_result(
             result, shots, expected_var, expected_mean, expected_eigs
         )
@@ -498,6 +498,19 @@ def result_types_noncommuting_testing(device: Device, run_kwargs: dict[str, Any]
     obs2_targets = [0, 2]
     obs3 = Observable.Y() @ Observable.Hermitian(array)
     obs3_targets = [0, 1, 2]
+    expected_mean1 = np.sin(theta) * np.sin(phi) * np.sin(varphi)
+    expected_var1 = (
+        8 * np.sin(theta) ** 2 * np.cos(2 * varphi) * np.sin(phi) ** 2
+        - np.cos(2 * (theta - phi))
+        - np.cos(2 * (theta + phi))
+        + 2 * np.cos(2 * theta)
+        + 2 * np.cos(2 * phi)
+        + 14
+    ) / 16
+
+    expected_mean2 = 0.849694136476246
+    expected_mean3 = 1.4499810303182408
+
     circuit = (
         get_result_types_three_qubit_circuit(theta, phi, varphi, obs1, obs1_targets, shots)
         .expectation(obs2, obs2_targets)
@@ -507,18 +520,6 @@ def result_types_noncommuting_testing(device: Device, run_kwargs: dict[str, Any]
     for task in tasks:
         result = device.run(task, **run_kwargs).result()
 
-        expected_mean1 = np.sin(theta) * np.sin(phi) * np.sin(varphi)
-        expected_var1 = (
-            8 * np.sin(theta) ** 2 * np.cos(2 * varphi) * np.sin(phi) ** 2
-            - np.cos(2 * (theta - phi))
-            - np.cos(2 * (theta + phi))
-            + 2 * np.cos(2 * theta)
-            + 2 * np.cos(2 * phi)
-            + 14
-        ) / 16
-
-        expected_mean2 = 0.849694136476246
-        expected_mean3 = 1.4499810303182408
         assert np.allclose(result.values[0], expected_var1)
         assert np.allclose(result.values[1], expected_mean1)
         assert np.allclose(result.values[2], expected_mean2)