fix: Python 3.11 syntax

pyproject.toml

@@ -89,7 +89,7 @@ target-version = "py311"
 line-length = 100
 
 [tool.ruff.lint]
-extend-select = ["I", "PERF"]
+extend-select = ["I", "PERF", "UP"]
 preview = false #TODO: set back to true
 
 [tool.ruff.lint.isort]

src/braket/experimental/algorithms/adaptive_shot_allocation/adaptive_allocator.py

@@ -1,15 +1,15 @@
 import random
 import warnings
+from collections.abc import Callable
 from functools import partial
-from typing import Callable, Dict, List, Tuple, Union
 
 import matplotlib.pyplot as plt
 import networkx as nx
 import numpy as np
 from networkx.algorithms import approximation
 
 # Type alias for measurement data structure (see AdaptiveShotAllocator.measurements)
-MeasurementData = List[List[Dict[Tuple[int, int], int]]]
+MeasurementData = list[list[dict[tuple[int, int], int]]]
 
 """
 Helper functions for Pauli operator commutation and Bayesian statistics calculations.
@@ -48,9 +48,7 @@ def commute(a: str, b: str, qwc: bool = True) -> bool:
 # BAYESIAN STATISTICS (closed formulas from Appendix B of arXiv:2110.15339v6)
 
 
-def term_variance_estimate(
-    term_idx: int, measurements: Union[MeasurementData, None] = None
-) -> float:
+def term_variance_estimate(term_idx: int, measurements: MeasurementData | None = None) -> float:
     """
     Estimate variance for a single Pauli term.
     See Eq 14 in Appendix B of "Adaptive Estimation of Quantum Observables (arXiv:2110.15339v6)
@@ -69,9 +67,7 @@ def term_variance_estimate(
     return 4 * ((x0 + 1) * (x1 + 1)) / ((x0 + x1 + 2) * (x0 + x1 + 3))
 
 
-def terms_covariance_estimate(
-    i: int, j: int, measurements: Union[MeasurementData, None] = None
-) -> float:
+def terms_covariance_estimate(i: int, j: int, measurements: MeasurementData | None = None) -> float:
     """
     Estimate covariance between two Pauli terms.
     See Eq 25-6 in Appendix B of "Adaptive Estimation of Quantum Observables (arXiv:2110.15339v6)
@@ -139,14 +135,14 @@ class AdaptiveShotAllocator:
     """
 
     num_terms: int
-    paulis: List[str]
-    coeffs: List[float]
+    paulis: list[str]
+    coeffs: list[float]
     graph: nx.Graph
-    cliq: List[List[int]]
+    cliq: list[list[int]]
     measurements: MeasurementData
-    shots: Union[List[int], None]
+    shots: list[int] | None
 
-    def __init__(self, paulis: List[str], coeffs: List[float]) -> None:
+    def __init__(self, paulis: list[str], coeffs: list[float]) -> None:
         """
         Initialize the AdaptiveShotAllocator with Pauli terms and their coefficients.
 
@@ -327,7 +323,7 @@ def _update_graph_weights(self) -> None:
             )
             self.graph[i][j]["weight"] = weight
 
-    def incremental_shot_allocation(self, num_shots: int) -> List[int]:
+    def incremental_shot_allocation(self, num_shots: int) -> list[int]:
         """
         Propose allocation of measurement shots to cliques using greedy minimization of
         the estimated error.
@@ -415,9 +411,7 @@ def error_estimate(self) -> float:
 
         return np.sqrt(variance_estimate)
 
-    def expectation_from_measurements(
-        self, measurements: Union[MeasurementData, None] = None
-    ) -> float:
+    def expectation_from_measurements(self, measurements: MeasurementData | None = None) -> float:
         """
         Calculate the energy expectation value from measurement results
         for the different Pauli string observables.
@@ -515,7 +509,7 @@ def _validate_measurements(self, measurements: MeasurementData) -> bool:
                         )
         return True
 
-    def shots_from_measurements(self, measurements: MeasurementData) -> List[int]:
+    def shots_from_measurements(self, measurements: MeasurementData) -> list[int]:
         """
         Extract the number of shots allocated to each clique from measurement data.
 

src/braket/experimental/algorithms/adaptive_shot_allocation/adaptive_allocator_braket_helpers.py

@@ -3,8 +3,6 @@
 experiments on Amazon Braket.
 """
 
-from typing import List, Union
-
 from braket.aws import AwsDevice
 from braket.circuits import Circuit, Observable
 from braket.devices import LocalSimulator
@@ -29,10 +27,10 @@ def observable_from_string(pauli_string: str) -> Observable:
 
 
 def run_fixed_allocation(
-    device: Union[LocalSimulator, AwsDevice],
+    device: LocalSimulator | AwsDevice,
     circuit: Circuit,
     estimator: AdaptiveShotAllocator,
-    shot_allocation: List[int],
+    shot_allocation: list[int],
 ) -> MeasurementData:
     """
     Run experiment with a specific shot allocation.
@@ -96,7 +94,7 @@ def run_fixed_allocation(
 
 
 def run_adaptive_allocation(
-    device: Union[LocalSimulator, AwsDevice],
+    device: LocalSimulator | AwsDevice,
     circuit: Circuit,
     estimator: AdaptiveShotAllocator,
     shots_per_round: int,

src/braket/experimental/algorithms/bells_inequality/bells_inequality.py

@@ -11,7 +11,6 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 from collections import Counter
-from typing import List, Tuple
 
 import numpy as np
 
@@ -26,7 +25,7 @@ def create_bell_inequality_circuits(
     angle_A: float = 0,
     angle_B: float = np.pi / 3,
     angle_C: float = 2 * np.pi / 3,
-) -> List[Circuit]:
+) -> list[Circuit]:
     """Create the three circuits for Bell's inequality. Default angles will give maximum violation
     of Bell's inequality.
 
@@ -48,10 +47,10 @@ def create_bell_inequality_circuits(
 
 
 def run_bell_inequality(
-    circuits: List[Circuit],
+    circuits: list[Circuit],
     device: Device,
     shots: int = 1_000,
-) -> List[QuantumTask]:
+) -> list[QuantumTask]:
     """Submit three Bell circuits to a device.
 
     Args:
@@ -67,8 +66,8 @@ def run_bell_inequality(
 
 
 def get_bell_inequality_results(
-    tasks: List[QuantumTask], verbose: bool = True
-) -> Tuple[List[Counter], float, float, float]:
+    tasks: list[QuantumTask], verbose: bool = True
+) -> tuple[list[Counter], float, float, float]:
     """Return Bell task results after post-processing.
 
     Args:

src/braket/experimental/algorithms/bernstein_vazirani/bernstein_vazirani.py

@@ -10,7 +10,6 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-from typing import Dict
 
 import numpy as np
 
@@ -61,7 +60,7 @@ def bernstein_vazirani_circuit(hidden_string: str) -> Circuit:
     return bv_circuit
 
 
-def get_bernstein_vazirani_results(task: QuantumTask) -> Dict[str, float]:
+def get_bernstein_vazirani_results(task: QuantumTask) -> dict[str, float]:
     """Return the probabilities and corresponding bitstrings.
 
     Args:

src/braket/experimental/algorithms/chsh_inequality/chsh_inequality.py

@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 
 from collections import Counter
-from typing import List, Tuple
 
 import numpy as np
 
@@ -32,7 +31,7 @@ def create_chsh_inequality_circuits(
     a2: float = 0,
     b1: float = np.pi / 4,
     b2: float = 3 * np.pi / 4,
-) -> List[Circuit]:
+) -> list[Circuit]:
     """Create the four circuits for CHSH inequality. Default angles will give maximum violation of
     the inequality.
 
@@ -55,10 +54,10 @@ def create_chsh_inequality_circuits(
 
 
 def run_chsh_inequality(
-    circuits: List[Circuit],
+    circuits: list[Circuit],
     device: Device,
     shots: int = 1_000,
-) -> List[QuantumTask]:
+) -> list[QuantumTask]:
     """Submit four CHSH circuits to a device.
 
     Args:
@@ -74,8 +73,8 @@ def run_chsh_inequality(
 
 
 def get_chsh_results(
-    tasks: List[QuantumTask], verbose: bool = True
-) -> Tuple[float, List[Counter], float, float, float]:
+    tasks: list[QuantumTask], verbose: bool = True
+) -> tuple[float, list[Counter], float, float, float]:
     """Return CHSH task results after post-processing.
 
     Args:

src/braket/experimental/algorithms/deutsch_jozsa/deutsch_jozsa.py

@@ -10,7 +10,6 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-from typing import Dict
 
 import numpy as np
 
@@ -109,7 +108,7 @@ def deutsch_jozsa(oracle: Circuit) -> Circuit:
     return circ
 
 
-def get_deutsch_jozsa_results(task: QuantumTask) -> Dict[str, float]:
+def get_deutsch_jozsa_results(task: QuantumTask) -> dict[str, float]:
     """Return the probabilities and corresponding bitstrings.
 
     Args:

src/braket/experimental/algorithms/grovers_search/grovers_search.py

@@ -1,5 +1,3 @@
-from typing import Tuple
-
 from braket.circuits import Circuit, circuit
 
 
@@ -99,7 +97,7 @@ def multi_control_not_constructor(
     n_qubit: int,
     decompose_ccnot: bool,
     is_outermost_call: bool = True,
-) -> Tuple[Circuit, int]:
+) -> tuple[Circuit, int]:
     """Recursive constructor of a multi-contol Not circuit (generalized Toffoli gate).
     Ref: https://arxiv.org/abs/1904.01671
 

src/braket/experimental/algorithms/hhl/hhl.py

@@ -23,7 +23,7 @@
     [2] Wikipedia: https://en.wikipedia.org/wiki/HHL_algorithm
 """
 
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 import numpy as np
 from qiskit.circuit import QuantumCircuit
@@ -47,7 +47,7 @@ def hhl_circuit(
     matrix: np.ndarray,
     b_vector: np.ndarray,
     num_clock_qubits: int = 2,
-    scaling_factor: Optional[float] = None,
+    scaling_factor: float | None = None,
 ) -> Circuit:
     """Construct the full HHL circuit for solving Ax = b.
 
@@ -147,7 +147,7 @@ def get_hhl_results(
     b_vector: np.ndarray,
     num_clock_qubits: int = 2,
     verbose: bool = False,
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
     """Post-process results from an HHL run.
 
     Extracts the solution state by post-selecting on the ancilla qubit
@@ -330,7 +330,7 @@ def _compute_hamiltonian_simulation(matrix: np.ndarray, t: float) -> np.ndarray:
 
 def _decompose_controlled_unitary(
     unitary: np.ndarray,
-    targets: List[int],
+    targets: list[int],
 ) -> Circuit:
     """Decompose a controlled-U gate into native 1q/2q gates via Qiskit transpilation.
 

src/braket/experimental/algorithms/qc_qmc/classical_qmc.py

@@ -1,8 +1,8 @@
 import copy
 import multiprocessing as mp
 import os
+from collections.abc import Callable
 from dataclasses import dataclass
-from typing import Callable, List, Tuple
 
 import pennylane as qml
 from openfermion.circuits.low_rank import low_rank_two_body_decomposition
@@ -17,11 +17,11 @@ class ChemicalProperties:
     nuclear_repulsion: float  # nuclear repulsion energy
     v_0: np.ndarray  # one-body term stored as np.ndarray with mean-field subtraction
     h_chem: np.ndarray  # one-body term stored as np.ndarray, without mean-field subtraction
-    v_gamma: List[np.ndarray]  # 1j * l_gamma
-    l_gamma: List[np.ndarray]  # Cholesky vector decomposed from two-body terms
+    v_gamma: list[np.ndarray]  # 1j * l_gamma
+    l_gamma: list[np.ndarray]  # Cholesky vector decomposed from two-body terms
     mf_shift: np.ndarray  # mean-field shift
-    lambda_l: List[np.ndarray]  # eigenvalues of Cholesky vectors
-    u_l: List[np.ndarray]  # eigenvectors of Cholesky vectors
+    lambda_l: list[np.ndarray]  # eigenvalues of Cholesky vectors
+    u_l: list[np.ndarray]  # eigenvectors of Cholesky vectors
 
 
 def classical_qmc(
@@ -31,7 +31,7 @@ def classical_qmc(
     trial: np.ndarray,
     prop: ChemicalProperties,
     max_pool: int = 8,
-) -> Tuple[float, float]:
+) -> tuple[float, float]:
     """Classical Auxiliary-Field Quantum Monte Carlo.
 
     Args:
@@ -82,7 +82,7 @@ def hartree_fock_energy(trial: np.ndarray, prop: ChemicalProperties) -> float:
     return e_hf
 
 
-def full_imag_time_evolution_wrapper(args: Tuple) -> Callable:
+def full_imag_time_evolution_wrapper(args: tuple) -> Callable:
     return full_imag_time_evolution(*args)
 
 
@@ -94,7 +94,7 @@ def full_imag_time_evolution(
     e_shift: float,
     walker: np.ndarray,
     weight: float,
-) -> Tuple[List[float], float]:
+) -> tuple[list[float], float]:
     """Imaginary time evolution of a single walker.
 
     Args:
@@ -128,7 +128,7 @@ def imag_time_propogator(
     weight: float,
     prop: ChemicalProperties,
     e_shift: float,
-) -> Tuple[float, np.ndarray, float]:
+) -> tuple[float, np.ndarray, float]:
     """Propagate a walker by one time step.
 
     Args:
@@ -203,7 +203,7 @@ def local_energy(h1e: np.ndarray, eri: np.ndarray, green_funcs: np.ndarray, enuc
     return e1 + e2 + enuc
 
 
-def reortho(A: np.ndarray) -> Tuple[np.ndarray, float]:
+def reortho(A: np.ndarray) -> tuple[np.ndarray, float]:
     """Reorthogonalise a MxN matrix A. Performs a QR decomposition of A. Note that for consistency
     elsewhere we want to preserve detR > 0 which is not guaranteed. We thus factor the signs of the
     diagonal of R into Q.
@@ -309,13 +309,13 @@ def chemistry_preparation(
 
 def propagate_walker(
     x: np.ndarray,
-    v_0: List[np.ndarray],
-    v_gamma: List[np.ndarray],
+    v_0: list[np.ndarray],
+    v_gamma: list[np.ndarray],
     mf_shift: np.ndarray,
     dtau: float,
     trial: np.ndarray,
     walker: np.ndarray,
-    green_funcs: List[np.ndarray],
+    green_funcs: list[np.ndarray],
 ) -> np.ndarray:
     r"""Update the walker forward in imaginary time.