Support for V2 primitives

.github/workflows/main.yml

@@ -312,4 +312,4 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: coveralls --service=github
-        shell: bash
+        shell: bash

.pylintdict

@@ -312,6 +312,7 @@ monte
 mosca
 mpl
 mprev
+msg
 multiclass
 multinomial
 multioutput
@@ -501,6 +502,7 @@ sparsearray
 spedalieri
 spsa
 sqrt
+stacklevel
 statefn
 statevector
 statevectors

qiskit_machine_learning/algorithms/inference/qbayesian.py

@@ -15,11 +15,17 @@
 
 import copy
 from typing import Tuple, Dict, Set, List
+
 from qiskit import QuantumCircuit, ClassicalRegister
 from qiskit.quantum_info import Statevector
-from qiskit.circuit.library import GroverOperator
-from qiskit.primitives import BaseSampler, Sampler
 from qiskit.circuit import Qubit
+from qiskit.circuit.library import GroverOperator
+from qiskit.primitives import BaseSampler, Sampler, BaseSamplerV2, BaseSamplerV1
+from qiskit.transpiler.passmanager import BasePassManager
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
+from qiskit.providers.fake_provider import GenericBackendV2
+
+from ...utils.deprecation import issue_deprecation_msg
 
 
 class QBayesian:
@@ -62,7 +68,8 @@ def __init__(
         *,
         limit: int = 10,
         threshold: float = 0.9,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSampler | BaseSamplerV2 | None = None,
+        pass_manager: BasePassManager | None = None,
     ):
         """
         Args:
@@ -83,15 +90,30 @@ def __init__(
         # Test valid input
         for qrg in circuit.qregs:
             if qrg.size > 1:
-                raise ValueError("Every register needs to be mapped to exactly one unique qubit")
+                raise ValueError("Every register needs to be mapped to exactly one unique qubit.")
+
         # Initialize parameter
         self._circ = circuit
         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",
+            )
+
         self._sampler = sampler
 
+        if pass_manager is None:
+            _backend = GenericBackendV2(num_qubits=max(circuit.num_qubits, 2))
+            pass_manager = generate_preset_pass_manager(optimization_level=1, backend=_backend)
+        self._pass_manager = pass_manager
+
         # Label of register mapped to its qubit
         self._label2qubit = {qrg.name: qrg[0] for qrg in self._circ.qregs}
         # Label of register mapped to its qubit index bottom up in significance
@@ -139,11 +161,34 @@ def _get_grover_op(self, evidence: Dict[str, int]) -> GroverOperator:
 
     def _run_circuit(self, circuit: QuantumCircuit) -> Dict[str, float]:
         """Run the quantum circuit with the sampler."""
-        # 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()
+        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
+            circuit_isa = self._pass_manager.run(circuit)
+            job = self._sampler.run([circuit_isa])
+            result = job.result()
+
+            bit_array = list(result[0].data.values())[0]
+            bitstring_counts = bit_array.get_counts()
+
+            # Normalize the counts to probabilities
+            total_shots = result[0].metadata["shots"]
+            counts = {k: v / total_shots for k, v in bitstring_counts.items()}
+
+            # Convert to quasi-probabilities
+            # counts = QuasiDistribution(probabilities)
+            # counts = {k: v for k, v in counts.items()}
+
         return counts
 
     def __power_grover(
@@ -360,12 +405,12 @@ def limit(self, limit: int):
         self._limit = limit
 
     @property
-    def sampler(self) -> BaseSampler:
+    def sampler(self) -> BaseSampler | BaseSamplerV2:
         """Returns the sampler primitive used to compute the samples."""
         return self._sampler
 
     @sampler.setter
-    def sampler(self, sampler: BaseSampler):
+    def sampler(self, sampler: BaseSampler | BaseSamplerV2):
         """Set the sampler primitive used to compute the samples."""
         self._sampler = sampler
 

qiskit_machine_learning/gradients/base/base_estimator_gradient.py

@@ -23,11 +23,13 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimator, BaseEstimatorV1
+from qiskit.primitives.base import BaseEstimatorV2
 from qiskit.primitives.utils import _circuit_key
 from qiskit.providers import Options
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 from qiskit.transpiler.passes import TranslateParameterizedGates
+from qiskit.transpiler.passmanager import BasePassManager
 
 from .estimator_gradient_result import EstimatorGradientResult
 from ..utils import (
@@ -37,7 +39,7 @@
     _make_gradient_parameters,
     _make_gradient_parameter_values,
 )
-
+from ...utils.deprecation import issue_deprecation_msg
 from ...algorithm_job import AlgorithmJob
 
 
@@ -46,13 +48,15 @@ class BaseEstimatorGradient(ABC):
 
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimator | BaseEstimatorV2,
         options: Options | None = None,
         derivative_type: DerivativeType = DerivativeType.REAL,
+        pass_manager: BasePassManager | None = None,
     ):
         r"""
         Args:
             estimator: The estimator used to compute the gradients.
+            pass_manager: pass manager for isa_circuit transpilation.
             options: Primitive backend runtime options used for circuit execution.
                 The order of priority is: options in ``run`` method > gradient's
                 default options > primitive's default setting.
@@ -68,7 +72,15 @@ def __init__(
                 gradient and this type is the only supported type for function-level schemes like
                 finite difference.
         """
+        if isinstance(estimator, BaseEstimatorV1):
+            issue_deprecation_msg(
+                msg="V1 Primitives are deprecated",
+                version="0.8.0",
+                remedy="Use V2 primitives for continued compatibility and support.",
+                period="4 months",
+            )
         self._estimator: BaseEstimator = estimator
+        self._pass_manager = pass_manager
         self._default_options = Options()
         if options is not None:
             self._default_options.update_options(**options)
@@ -92,7 +104,7 @@ def run(
         self,
         circuits: Sequence[QuantumCircuit],
         observables: Sequence[BaseOperator],
-        parameter_values: Sequence[Sequence[float]],
+        parameter_values: Sequence[Sequence[float]] | np.ndarray,
         parameters: Sequence[Sequence[Parameter] | None] | None = None,
         **options,
     ) -> AlgorithmJob:
@@ -157,7 +169,7 @@ def _run(
         self,
         circuits: Sequence[QuantumCircuit],
         observables: Sequence[BaseOperator],
-        parameter_values: Sequence[Sequence[float]],
+        parameter_values: Sequence[Sequence[float]] | np.ndarray,
         parameters: Sequence[Sequence[Parameter]],
         **options,
     ) -> EstimatorGradientResult:
@@ -167,7 +179,7 @@ def _run(
     def _preprocess(
         self,
         circuits: Sequence[QuantumCircuit],
-        parameter_values: Sequence[Sequence[float]],
+        parameter_values: Sequence[Sequence[float]] | np.ndarray,
         parameters: Sequence[Sequence[Parameter]],
         supported_gates: Sequence[str],
     ) -> tuple[Sequence[QuantumCircuit], Sequence[Sequence[float]], Sequence[Sequence[Parameter]]]:
@@ -209,7 +221,7 @@ def _postprocess(
         self,
         results: EstimatorGradientResult,
         circuits: Sequence[QuantumCircuit],
-        parameter_values: Sequence[Sequence[float]],
+        parameter_values: Sequence[Sequence[float]] | np.ndarray,
         parameters: Sequence[Sequence[Parameter]],
     ) -> EstimatorGradientResult:
         """Postprocess the gradients. This method computes the gradient of the original circuits
@@ -269,7 +281,7 @@ def _postprocess(
     def _validate_arguments(
         circuits: Sequence[QuantumCircuit],
         observables: Sequence[BaseOperator],
-        parameter_values: Sequence[Sequence[float]],
+        parameter_values: Sequence[Sequence[float]] | np.ndarray,
         parameters: Sequence[Sequence[Parameter]],
     ) -> None:
         """Validate the arguments of the ``run`` method.

qiskit_machine_learning/gradients/base/base_sampler_gradient.py

@@ -22,10 +22,11 @@
 from copy import copy
 
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSampler, BaseSamplerV1
 from qiskit.primitives.utils import _circuit_key
 from qiskit.providers import Options
 from qiskit.transpiler.passes import TranslateParameterizedGates
+from qiskit.transpiler.passmanager import BasePassManager
 
 from .sampler_gradient_result import SamplerGradientResult
 from ..utils import (
@@ -34,14 +35,20 @@
     _make_gradient_parameters,
     _make_gradient_parameter_values,
 )
-
+from ...utils.deprecation import issue_deprecation_msg
 from ...algorithm_job import AlgorithmJob
 
 
 class BaseSamplerGradient(ABC):
     """Base class for a ``SamplerGradient`` to compute the gradients of the sampling probability."""
 
-    def __init__(self, sampler: BaseSampler, options: Options | None = None):
+    def __init__(
+        self,
+        sampler: BaseSampler,
+        options: Options | None = None,
+        len_quasi_dist: int | None = None,
+        pass_manager: BasePassManager | None = None,
+    ):
         """
         Args:
             sampler: The sampler used to compute the gradients.
@@ -50,7 +57,16 @@ def __init__(self, sampler: BaseSampler, options: Options | None = None):
                 default options > primitive's default setting.
                 Higher priority setting overrides lower priority setting
         """
+        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",
+            )
         self._sampler: BaseSampler = sampler
+        self._pass_manager = pass_manager
+        self._len_quasi_dist = len_quasi_dist
         self._default_options = Options()
         if options is not None:
             self._default_options.update_options(**options)

qiskit_machine_learning/gradients/lin_comb/lin_comb_estimator_gradient.py

@@ -98,7 +98,7 @@ def _run(
         self,
         circuits: Sequence[QuantumCircuit],
         observables: Sequence[BaseOperator],
-        parameter_values: Sequence[Sequence[float]],
+        parameter_values: Sequence[Sequence[float]] | np.ndarray,
         parameters: Sequence[Sequence[Parameter]],
         **options,
     ) -> EstimatorGradientResult: