Type modernization

docs/tutorials/01_algorithms_introduction.ipynb

@@ -322,7 +322,7 @@
    "source": [
     "def callback(**kwargs):\n",
     "    if kwargs[\"count\"] == 0:\n",
-    "        print(f\"Callback function has been called!\")"
+    "        print(\"Callback function has been called!\")"
    ]
   },
   {

docs/tutorials/05_qaoa.ipynb

@@ -208,7 +208,6 @@
    ],
    "source": [
     "from qiskit.primitives import StatevectorSampler\n",
-    "from qiskit.quantum_info import Pauli\n",
     "from qiskit.result import QuasiDistribution\n",
     "\n",
     "from qiskit_algorithms import QAOA\n",
@@ -375,7 +374,7 @@
    "source": [
     "def callback(**kwargs):\n",
     "    if kwargs[\"count\"] == 0:\n",
-    "        print(f\"Callback function has been called!\")"
+    "        print(\"Callback function has been called!\")"
    ]
   },
   {

docs/tutorials/06_grover.ipynb

@@ -747,7 +747,7 @@
    "source": [
     "def callback(**kwargs):\n",
     "    if kwargs[\"count\"] == 0:\n",
-    "        print(f\"Callback function has been called!\")"
+    "        print(\"Callback function has been called!\")"
    ]
   },
   {

docs/tutorials/10_pvqd.ipynb

@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -701,7 +701,6 @@
     "\n",
     "def exact(final_time, timestep, hamiltonian, initial_state):\n",
     "    \"\"\"Get the exact values for energy and the observable.\"\"\"\n",
-    "    O = observable.to_matrix()\n",
     "    H = hamiltonian.to_matrix()\n",
     "\n",
     "    energ, magn = [], []  # list of energies and magnetizations evaluated at timesteps timestep\n",

qiskit_algorithms/amplitude_amplifiers/amplification_problem.py

@@ -14,7 +14,7 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-from typing import Any, List, cast
+from typing import Any, cast
 
 from qiskit.circuit import QuantumCircuit, Gate
 from qiskit.circuit.library import GroverOperator
@@ -177,10 +177,10 @@ def is_good_state(self) -> Callable[[str], bool]:
             return self._is_good_state  # returns None if no is_good_state arg has been set
         elif isinstance(self._is_good_state, list):
             if all(isinstance(good_bitstr, str) for good_bitstr in self._is_good_state):
-                return lambda bitstr: bitstr in cast(List[str], self._is_good_state)
+                return lambda bitstr: bitstr in cast(list[str], self._is_good_state)
             else:
                 return lambda bitstr: all(
-                    bitstr[good_index] == "1" for good_index in cast(List[int], self._is_good_state)
+                    bitstr[good_index] == "1" for good_index in cast(list[int], self._is_good_state)
                 )
 
         return lambda bitstr: bitstr in cast(Statevector, self._is_good_state).probabilities_dict()

qiskit_algorithms/amplitude_amplifiers/amplitude_amplifier.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2023.
+# (C) Copyright IBM 2021, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -24,7 +24,7 @@ class AmplitudeAmplifier(ABC):
     """The interface for amplification algorithms."""
 
     @abstractmethod
-    def amplify(self, amplification_problem: AmplificationProblem) -> "AmplitudeAmplifierResult":
+    def amplify(self, amplification_problem: AmplificationProblem) -> AmplitudeAmplifierResult:
         """Run the amplification algorithm.
 
         Args:

qiskit_algorithms/amplitude_amplifiers/grover.py

@@ -192,7 +192,7 @@ def sampler(self, sampler: BaseSamplerV2) -> None:
         """
         self._sampler = sampler
 
-    def amplify(self, amplification_problem: AmplificationProblem) -> "GroverResult":
+    def amplify(self, amplification_problem: AmplificationProblem) -> GroverResult:
         """Run the Grover algorithm.
 
         Args:

qiskit_algorithms/amplitude_estimators/ae.py

@@ -228,7 +228,7 @@ def _evaluate_count_results(self, counts) -> tuple[dict[float, float], dict[int,
 
     @staticmethod
     def compute_mle(
-        result: "AmplitudeEstimationResult", apply_post_processing: bool = False
+        result: AmplitudeEstimationResult, apply_post_processing: bool = False
     ) -> float:
         """Compute the Maximum Likelihood Estimator (MLE).
 
@@ -285,7 +285,7 @@ def loglikelihood(a):
 
         return a_opt
 
-    def estimate(self, estimation_problem: EstimationProblem) -> "AmplitudeEstimationResult":
+    def estimate(self, estimation_problem: EstimationProblem) -> AmplitudeEstimationResult:
         """Run the amplitude estimation algorithm on provided estimation problem.
 
         Args:
@@ -379,7 +379,7 @@ def estimate(self, estimation_problem: EstimationProblem) -> "AmplitudeEstimatio
 
     @staticmethod
     def compute_confidence_interval(
-        result: "AmplitudeEstimationResult",
+        result: AmplitudeEstimationResult,
         alpha: float = 0.05,
         kind: str = "likelihood_ratio",
     ) -> tuple[float, float]:

qiskit_algorithms/amplitude_estimators/amplitude_estimator.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -24,7 +24,7 @@ class AmplitudeEstimator(ABC):
     """The Amplitude Estimation interface."""
 
     @abstractmethod
-    def estimate(self, estimation_problem: EstimationProblem) -> "AmplitudeEstimatorResult":
+    def estimate(self, estimation_problem: EstimationProblem) -> AmplitudeEstimatorResult:
         """Run the amplitude estimation algorithm.
 
         Args:

qiskit_algorithms/amplitude_estimators/estimation_problem.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2024.
+# (C) Copyright IBM 2020, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -202,7 +202,7 @@ def grover_operator(self, grover_operator: QuantumCircuit | None) -> None:
         """
         self._grover_operator = grover_operator
 
-    def rescale(self, scaling_factor: float) -> "EstimationProblem":
+    def rescale(self, scaling_factor: float) -> EstimationProblem:
         """Rescale the good state amplitude in the estimation problem.
 
         Args:

qiskit_algorithms/amplitude_estimators/fae.py

@@ -13,7 +13,7 @@
 """Faster Amplitude Estimation."""
 
 from __future__ import annotations
-from typing import cast, Tuple, Any
+from typing import cast, Any
 import warnings
 import numpy as np
 
@@ -182,7 +182,7 @@ def construct_circuit(
 
         return circuit
 
-    def estimate(self, estimation_problem: EstimationProblem) -> "FasterAmplitudeEstimationResult":
+    def estimate(self, estimation_problem: EstimationProblem) -> FasterAmplitudeEstimationResult:
         """Run the amplitude estimation algorithm on provided estimation problem.
 
         Args:
@@ -264,7 +264,7 @@ def cos_estimate(power, shots):
         result.estimation_processed = problem.post_processing(value)  # type: ignore[assignment]
         result.confidence_interval = value_ci
         result.confidence_interval_processed = cast(
-            Tuple[float, float], (problem.post_processing(x) for x in value_ci)
+            tuple[float, float], (problem.post_processing(x) for x in value_ci)
         )
         result.theta_intervals = theta_cis
 

qiskit_algorithms/amplitude_estimators/iae.py

@@ -13,7 +13,8 @@
 """The Iterative Quantum Amplitude Estimation Algorithm."""
 
 from __future__ import annotations
-from typing import cast, Callable, Tuple, Any
+from typing import cast, Any
+from collections.abc import Callable
 import warnings
 import numpy as np
 from scipy.stats import beta
@@ -268,9 +269,7 @@ def _good_state_probability(
 
         return int(one_counts), one_counts / sum(counts_dict.values())
 
-    def estimate(
-        self, estimation_problem: EstimationProblem
-    ) -> "IterativeAmplitudeEstimationResult":
+    def estimate(self, estimation_problem: EstimationProblem) -> IterativeAmplitudeEstimationResult:
         """Run the amplitude estimation algorithm on provided estimation problem.
 
         Args:
@@ -384,7 +383,7 @@ def estimate(
             a_intervals.append([a_l, a_u])
 
         # get the latest confidence interval for the estimate of a
-        confidence_interval = cast(Tuple[float, float], a_intervals[-1])
+        confidence_interval = cast(tuple[float, float], a_intervals[-1])
 
         # the final estimate is the mean of the confidence interval
         estimation = np.mean(confidence_interval)

qiskit_algorithms/amplitude_estimators/mlae.py

@@ -13,14 +13,13 @@
 """The Maximum Likelihood Amplitude Estimation algorithm."""
 
 from __future__ import annotations
-from collections.abc import Sequence
-from typing import Callable, List, Tuple, cast, Any
+from collections.abc import Callable, Sequence
+from typing import cast, Any
 import warnings
 
 import numpy as np
 from scipy.optimize import brute
 from scipy.stats import norm, chi2
-
 from qiskit import ClassicalRegister, QuantumRegister, QuantumCircuit
 from qiskit.primitives import BaseSamplerV2, StatevectorSampler
 
@@ -29,7 +28,8 @@
 from ..custom_types import Transpiler
 from ..exceptions import AlgorithmError
 
-MINIMIZER = Callable[[Callable[[float], float], List[Tuple[float, float]]], float]
+
+MINIMIZER = Callable[[Callable[[float], float], list[tuple[float, float]]], float]
 
 
 class MaximumLikelihoodAmplitudeEstimation(AmplitudeEstimator):
@@ -181,7 +181,7 @@ def construct_circuits(
 
     @staticmethod
     def compute_confidence_interval(
-        result: "MaximumLikelihoodAmplitudeEstimationResult",
+        result: MaximumLikelihoodAmplitudeEstimationResult,
         alpha: float,
         kind: str = "fisher",
         apply_post_processing: bool = False,
@@ -269,7 +269,7 @@ def loglikelihood(theta):
 
     def estimate(
         self, estimation_problem: EstimationProblem
-    ) -> "MaximumLikelihoodAmplitudeEstimationResult":
+    ) -> MaximumLikelihoodAmplitudeEstimationResult:
         """Run the amplitude estimation algorithm on provided estimation problem.
 
         Args:
@@ -320,7 +320,7 @@ def estimate(
         num_state_qubits = circuits[0].num_qubits - circuits[0].num_ancillas
 
         theta, good_counts = cast(
-            Tuple[float, List[float]],
+            tuple[float, list[float]],
             self.compute_mle(result.circuit_results, estimation_problem, num_state_qubits, True),
         )
 
@@ -424,7 +424,7 @@ def _safe_max(
 
 
 def _compute_fisher_information(
-    result: "MaximumLikelihoodAmplitudeEstimationResult",
+    result: MaximumLikelihoodAmplitudeEstimationResult,
     num_sum_terms: int | None = None,
     observed: bool = False,
 ) -> float:
@@ -558,7 +558,7 @@ def loglikelihood(theta, one_counts, all_counts):
     # then yield [0, pi/2]
     confint = [_safe_min(above_thres, default=0), _safe_max(above_thres, default=np.pi / 2)]
     mapped_confint = cast(
-        Tuple[float, float], tuple(result.post_processing(np.sin(bound) ** 2) for bound in confint)
+        tuple[float, float], tuple(result.post_processing(np.sin(bound) ** 2) for bound in confint)
     )
 
     return mapped_confint

qiskit_algorithms/custom_types.py

@@ -13,16 +13,29 @@
 """Types used by the qiskit-algorithms package."""
 from __future__ import annotations
 
-from typing import Any, Protocol, Union
+from typing import Any, Protocol, TypeAlias, TypeVar, overload
 
 from qiskit import QuantumCircuit
 
-_Circuits = Union[list[QuantumCircuit], QuantumCircuit]
+_T = TypeVar("_T")  # Pylint does not allow single character class names.
+ListOrDict = list[_T | None] | dict[str, _T]
+
+_Circuits: TypeAlias = list[QuantumCircuit] | QuantumCircuit
 
 
 class Transpiler(Protocol):
     """A Generic type to represent a transpiler."""
 
-    def run(self, circuits: _Circuits, **options: Any) -> _Circuits:
+    @overload
+    def run(self, circuits: list[QuantumCircuit], /, **options: Any) -> list[QuantumCircuit]:
+        """Transpile a list of quantum circuits."""
+        ...
+
+    @overload
+    def run(self, circuits: QuantumCircuit, /, **options: Any) -> QuantumCircuit:
+        """Transpile a single quantum circuit."""
+        ...
+
+    def run(self, circuits: _Circuits, /, **options: Any) -> _Circuits:
         """Transpile a circuit or a list of quantum circuits."""
         pass

qiskit_algorithms/eigensolvers/eigensolver.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,7 +21,7 @@
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from ..algorithm_result import AlgorithmResult
-from ..list_or_dict import ListOrDict
+from ..custom_types import ListOrDict
 
 
 class Eigensolver(ABC):
@@ -37,7 +37,7 @@ def compute_eigenvalues(
         self,
         operator: BaseOperator,
         aux_operators: ListOrDict[BaseOperator] | None = None,
-    ) -> "EigensolverResult":
+    ) -> EigensolverResult:
         """
         Computes the minimum eigenvalue. The ``operator`` and ``aux_operators`` are supplied here.
         While an ``operator`` is required by algorithms, ``aux_operators`` are optional.

qiskit_algorithms/eigensolvers/numpy_eigensolver.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,8 +14,8 @@
 
 from __future__ import annotations
 
-from collections.abc import Iterable
-from typing import Callable, Union, Tuple, Dict, List, Optional, cast
+from collections.abc import Callable, Iterable
+from typing import cast
 import logging
 import numpy as np
 from scipy import sparse as scisparse
@@ -26,12 +26,12 @@
 from qiskit_algorithms.utils.validation import validate_min
 from .eigensolver import Eigensolver, EigensolverResult
 from ..exceptions import AlgorithmError
-from ..list_or_dict import ListOrDict
+from ..custom_types import ListOrDict
 
 logger = logging.getLogger(__name__)
 
 FilterType = Callable[
-    [Union[List, np.ndarray], float, Optional[ListOrDict[Tuple[float, Dict[str, float]]]]], bool
+    [list | np.ndarray, float, ListOrDict[tuple[float, dict[str, float]]] | None], bool
 ]
 
 
@@ -162,9 +162,9 @@ def _solve_sparse(op_matrix: scisparse.csr_matrix, k: int) -> tuple[np.ndarray,
     def _solve_dense(op_matrix: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
         if op_matrix.all() == op_matrix.conj().T.all():
             # Operator is Hermitian
-            return cast(Tuple[np.ndarray, np.ndarray], np.linalg.eigh(op_matrix))
+            return cast(tuple[np.ndarray, np.ndarray], np.linalg.eigh(op_matrix))
         else:
-            return cast(Tuple[np.ndarray, np.ndarray], np.linalg.eig(op_matrix))
+            return cast(tuple[np.ndarray, np.ndarray], np.linalg.eig(op_matrix))
 
     @staticmethod
     def _eval_aux_operators(