Migrate the library to Qiskit 2.0

.pylintdict

@@ -357,6 +357,7 @@ nfevs
 nft
 nielsen
 njev
+nlocal
 nlopt
 nn
 noancilla
@@ -511,7 +512,7 @@ scipy
 sdg
 seealso
 semidefinite
-sep 
+sep
 seperate
 seperable
 serializable

qiskit_machine_learning/algorithm_job.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
@@ -29,17 +29,7 @@ def submit(self) -> None:
         """
         Submit the job for execution.
 
-        For V1 primitives, Qiskit ``PrimitiveJob`` subclassed JobV1 and defined ``submit()``.
-        ``PrimitiveJob`` was updated for V2 primitives, no longer subclasses ``JobV1``, and
-        now has a private ``_submit()`` method, with ``submit()`` being deprecated as of
-        Qiskit version 0.46. This maintains the ``submit()`` for ``AlgorithmJob`` here as
-        it's called in many places for such a job. An alternative could be to make
-        0.46 the required minimum version and alter all algorithm's call sites to use
-        ``_submit()`` and make this an empty class again as it once was. For now this
-        way maintains compatibility with the current min version of 0.44.
+        Since the library has been migrated to Qiskit v2.1, it is no longer necessary to
+        keep the :meth:``JobV1.submit()`` for the exception handling.
         """
-        # TODO: Considering changing this in the future - see above docstring.
-        try:
-            super()._submit()
-        except AttributeError:
-            super().submit()  # pylint: disable=no-member
+        super()._submit()

qiskit_machine_learning/algorithms/classifiers/__init__.py

@@ -13,8 +13,8 @@
 """Classifiers Package"""
 
 from .neural_network_classifier import NeuralNetworkClassifier
-from .qsvc import QSVC
 from .pegasos_qsvc import PegasosQSVC
+from .qsvc import QSVC
 from .vqc import VQC
 
 __all__ = ["NeuralNetworkClassifier", "QSVC", "PegasosQSVC", "VQC"]

qiskit_machine_learning/algorithms/classifiers/vqc.py

@@ -12,19 +12,18 @@
 """An implementation of variational quantum classifier."""
 
 from __future__ import annotations
+
 from typing import Callable
 
 import numpy as np
-
 from qiskit import QuantumCircuit
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSamplerV2
 from qiskit.transpiler.passmanager import BasePassManager
 
 from ...neural_networks import SamplerQNN
-from ...optimizers import Optimizer, OptimizerResult, Minimizer
+from ...optimizers import Minimizer, Optimizer, OptimizerResult
 from ...utils import derive_num_qubits_feature_map_ansatz
 from ...utils.loss_functions import Loss
-
 from .neural_network_classifier import NeuralNetworkClassifier
 
 
@@ -58,7 +57,7 @@ def __init__(
         initial_point: np.ndarray | None = None,
         callback: Callable[[np.ndarray, float], None] | None = None,
         *,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,  # change: BaseSampler is migrated to BaseSamplerV2
         interpret: Callable[[int], int | tuple[int, ...]] | None = None,
         output_shape: int | None = None,
         pass_manager: BasePassManager | None = None,
@@ -108,7 +107,7 @@ def __init__(
         """
 
         num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(
-            num_qubits, feature_map, ansatz, use_methods=True
+            num_qubits, feature_map, ansatz
         )
 
         if output_shape is None:

qiskit_machine_learning/algorithms/inference/qbayesian.py

@@ -14,17 +14,18 @@
 from __future__ import annotations
 
 import copy
-from typing import Tuple, Dict, Set, List
+from typing import Dict, Set
 
-from qiskit import QuantumCircuit, ClassicalRegister
-from qiskit.quantum_info import Statevector
+from qiskit import ClassicalRegister, QuantumCircuit
 from qiskit.circuit import Qubit
 from qiskit.circuit.library import grover_operator
-from qiskit.primitives import BaseSampler, Sampler, BaseSamplerV2, BaseSamplerV1
-from qiskit.transpiler.passmanager import BasePassManager
+from qiskit.primitives import (
+    BaseSamplerV2,
+    StatevectorSampler,
+)
+from qiskit.quantum_info import Statevector
 from qiskit.result import QuasiDistribution
-
-from ...utils.deprecation import issue_deprecation_msg
+from qiskit.transpiler.passmanager import BasePassManager
 
 
 class QBayesian:
@@ -67,7 +68,7 @@ def __init__(
         *,
         limit: int = 10,
         threshold: float = 0.9,
-        sampler: BaseSampler | BaseSamplerV2 | None = None,
+        sampler: BaseSamplerV2 | None = None,
         pass_manager: BasePassManager | None = None,
     ):
         """
@@ -96,15 +97,7 @@ def __init__(
         self._limit = limit
         self._threshold = threshold
         if sampler is None:
-            sampler = Sampler()
-
-        if isinstance(sampler, BaseSamplerV1):
-            issue_deprecation_msg(
-                msg="V1 Primitives are deprecated",
-                version="0.8.0",
-                remedy="Use V2 primitives for continued compatibility and support.",
-                period="4 months",
-            )
+            sampler = StatevectorSampler()
 
         self._sampler = sampler
 
@@ -167,40 +160,28 @@ def _run_circuit(self, circuit: QuantumCircuit) -> Dict[str, float]:
         """Run the quantum circuit with the sampler."""
         counts = {}
 
-        if isinstance(self._sampler, BaseSampler):
-            # Sample from circuit
-            job = self._sampler.run(circuit)
-            result = job.result()
-
-            # Get the counts of quantum state results
-            counts = result.quasi_dists[0].nearest_probability_distribution().binary_probabilities()
-
-        elif isinstance(self._sampler, BaseSamplerV2):
-            # Sample from circuit
-            if self._pass_manager is not None:
-                circuit = self._pass_manager.run(circuit)
-            job = self._sampler.run([circuit])
-            result = job.result()
-
-            bit_array = list(result[0].data.values())[0]
-            bitstring_counts = bit_array.get_counts()
+        # Sample from circuit
+        if self._pass_manager is not None:
+            circuit = self._pass_manager.run(circuit)
+        job = self._sampler.run([circuit])
+        result = job.result()
 
-            # Normalize the counts to probabilities
-            total_shots = sum(bitstring_counts.values())
-            probabilities = {k: v / total_shots for k, v in bitstring_counts.items()}
-            # Convert to quasi-probabilities
-            quasi_dist = QuasiDistribution(probabilities)
-            binary_prob = quasi_dist.nearest_probability_distribution().binary_probabilities()
-            counts = {k: v for k, v in binary_prob.items() if int(k) < 2**self.num_virtual_qubits}
+        bit_array = list(result[0].data.values())[0]
+        bitstring_counts = bit_array.get_counts()
 
-            # counts = QuasiDistribution(probabilities)
-            # counts = {k: v for k, v in counts.items()}
+        # Normalize the counts to probabilities
+        total_shots = sum(bitstring_counts.values())
+        probabilities = {k: v / total_shots for k, v in bitstring_counts.items()}
+        # Convert to quasi-probabilities
+        quasi_dist = QuasiDistribution(probabilities)
+        binary_prob = quasi_dist.nearest_probability_distribution().binary_probabilities()
+        counts = {k: v for k, v in binary_prob.items() if int(k) < 2**self.num_virtual_qubits}
 
         return counts
 
     def __power_grover(
         self, grover_op: QuantumCircuit, evidence: Dict[str, int], k: int
-    ) -> Tuple[QuantumCircuit, Set[Tuple[Qubit, int]]]:
+    ) -> tuple[QuantumCircuit, Set[tuple[Qubit, int]]]:
         """
         Applies the Grover operator to the quantum circuit 2^k times, measures the evidence qubits,
         and returns a tuple containing the updated quantum circuit and a set of the measured
@@ -246,7 +227,7 @@ def __power_grover(
         }
         return qc, e_meas
 
-    def _format_samples(self, samples: Dict[str, float], evidence: List[str]) -> Dict[str, float]:
+    def _format_samples(self, samples: Dict[str, float], evidence: list[str]) -> Dict[str, float]:
         """Transforms samples keys back to their variables names."""
         f_samples: Dict[str, float] = {}
         for smpl_key, smpl_val in samples.items():
@@ -295,7 +276,7 @@ def rejection_sampling(
             grover_op = self._get_grover_op(evidence)
             # Amplitude amplification
             true_e = {(self._label2qubit[e_key], e_val) for e_key, e_val in evidence.items()}
-            meas_e: Set[Tuple[str, int]] = set()
+            meas_e: Set[tuple[str, int]] = set()
             best_qc, best_inter = QuantumCircuit(), -1
             self._converged = False
             k = -1
@@ -412,12 +393,12 @@ def limit(self, limit: int):
         self._limit = limit
 
     @property
-    def sampler(self) -> BaseSampler | BaseSamplerV2:
+    def sampler(self) -> BaseSamplerV2:
         """Returns the sampler primitive used to compute the samples."""
         return self._sampler
 
     @sampler.setter
-    def sampler(self, sampler: BaseSampler | BaseSamplerV2):
+    def sampler(self, sampler: BaseSamplerV2):
         """Set the sampler primitive used to compute the samples."""
         self._sampler = sampler
 

qiskit_machine_learning/algorithms/regressors/__init__.py

@@ -12,8 +12,8 @@
 
 """Regressors Package"""
 
-from .qsvr import QSVR
 from .neural_network_regressor import NeuralNetworkRegressor
+from .qsvr import QSVR
 from .vqr import VQR
 
 __all__ = ["QSVR", "VQR", "NeuralNetworkRegressor"]

qiskit_machine_learning/algorithms/regressors/vqr.py

@@ -16,15 +16,15 @@
 
 import numpy as np
 from qiskit import QuantumCircuit
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 from qiskit.transpiler.passmanager import BasePassManager
 
-from .neural_network_regressor import NeuralNetworkRegressor
 from ...neural_networks import EstimatorQNN
-from ...optimizers import Optimizer, Minimizer
+from ...optimizers import Minimizer, Optimizer
 from ...utils import derive_num_qubits_feature_map_ansatz
 from ...utils.loss_functions import Loss
+from .neural_network_regressor import NeuralNetworkRegressor
 
 
 class VQR(NeuralNetworkRegressor):
@@ -43,7 +43,7 @@ def __init__(
         initial_point: np.ndarray | None = None,
         callback: Callable[[np.ndarray, float], None] | None = None,
         *,
-        estimator: BaseEstimator | None = None,
+        estimator: BaseEstimatorV2 | None = None,
         pass_manager: BasePassManager | None = None,
     ) -> None:
         r"""
@@ -94,7 +94,7 @@ def __init__(
         self._estimator = estimator
 
         num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(
-            num_qubits, feature_map, ansatz, use_methods=True
+            num_qubits, feature_map, ansatz
         )
 
         # construct circuit

qiskit_machine_learning/circuit/library/qnn_circuit.py

@@ -12,11 +12,10 @@
 
 """The QNN circuit."""
 from __future__ import annotations
-from typing import List
 
-from qiskit.circuit import QuantumRegister, QuantumCircuit
-from qiskit.circuit.parametertable import ParameterView
+from qiskit.circuit import QuantumCircuit, QuantumRegister
 from qiskit.circuit.library import BlueprintCircuit
+from qiskit.circuit.parametertable import ParameterView
 
 from qiskit_machine_learning import QiskitMachineLearningError
 
@@ -119,7 +118,7 @@ def qnn_circuit(
     """
     # Check if circuit is constructed with valid configuration and set properties accordingly.
     num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(
-        num_qubits, feature_map, ansatz, use_methods=True
+        num_qubits, feature_map, ansatz
     )
     qc = QuantumCircuit(num_qubits)
     qc.compose(feature_map, inplace=True)
@@ -274,7 +273,7 @@ def num_qubits(self, num_qubits: int) -> None:
         if self.num_qubits != num_qubits:
             # invalidate the circuit
             self._invalidate()
-            self.qregs: List[QuantumRegister] = []
+            self.qregs: list[QuantumRegister] = []
             if num_qubits is not None and num_qubits > 0:
                 self.qregs = [QuantumRegister(num_qubits, name="q")]
                 (

qiskit_machine_learning/circuit/library/raw_feature_vector.py

@@ -12,7 +12,7 @@
 
 """The raw feature vector circuit."""
 
-from typing import Optional, List
+from typing import Optional
 import numpy as np
 from qiskit.exceptions import QiskitError
 from qiskit.circuit import (
@@ -201,7 +201,7 @@ def num_qubits(self, num_qubits: int) -> None:
         if self.num_qubits != num_qubits:
             # invalidate the circuit
             self._invalidate()
-            self.qregs: List[QuantumRegister] = []
+            self.qregs: list[QuantumRegister] = []
             if num_qubits is not None and num_qubits > 0:
                 self.qregs = [QuantumRegister(num_qubits, name="q")]