Merge branch 'gradients-primitives' into grad-test

.github/workflows/randomized_tests.yml

@@ -25,7 +25,7 @@ jobs:
       - name: Run randomized tests
         run: make test_randomized
         env:
-          RUST_BACKTRACE=1
+          RUST_BACKTRACE: 1
       - name: Create comment on failed test run
         if: ${{ failure() }}
         uses: peter-evans/create-or-update-comment@v1

qiskit/algorithms/gradients/base_estimator_gradient.py

@@ -68,8 +68,8 @@ def run(
             circuits: The list of quantum circuits to compute the gradients.
             observables: The list of observables.
             parameter_values: The list of parameter values to be bound to the circuit.
-            parameters: The Sequence of Sequence of Parameters to calculate only the gradients of
-                the specified parameters. Each Sequence of Parameters corresponds to a circuit in
+            parameters: The sequence of parameters to calculate only the gradients of
+                the specified parameters. Each sequence of parameters corresponds to a circuit in
                 ``circuits``. Defaults to None, which means that the gradients of all parameters in
                 each circuit are calculated.
             run_options: Backend runtime options used for circuit execution. The order of priority is:

qiskit/algorithms/gradients/base_sampler_gradient.py

@@ -59,8 +59,8 @@ def run(
         Args:
             circuits: The list of quantum circuits to compute the gradients.
             parameter_values: The list of parameter values to be bound to the circuit.
-            parameters: The Sequence of Sequence of Parameters to calculate only the gradients of
-                the specified parameters. Each Sequence of Parameters corresponds to a circuit in
+            parameters: The sequence of parameters to calculate only the gradients of
+                the specified parameters. Each sequence of parameters corresponds to a circuit in
                 ``circuits``. Defaults to None, which means that the gradients of all parameters in
                 each circuit are calculated.
             run_options: Backend runtime options used for circuit execution. The order of priority is:
@@ -105,7 +105,7 @@ def _validate_arguments(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter] | None] | None = None,
-    ):
+    ) -> None:
         """Validate the arguments of the ``run`` method.
 
         Args:

qiskit/algorithms/gradients/estimator_gradient_result.py

@@ -23,15 +23,12 @@
 
 @dataclass(frozen=True)
 class EstimatorGradientResult:
-    """Result of ``EstimatorGradient``.
-
-    Args:
-        gradients: The gradients of the expectation values.
-        metadata: Additional information about the job.
-        run_options: run_options for the estimator. Currently, estimator's default run_options is not
-        included.
-    """
+    """Result of EstimatorGradient."""
 
     gradients: list[np.ndarray]
+    """The gradients of the expectation values."""
     metadata: list[dict[str, Any]]
+    """Additional information about the job."""
     run_options: dict[str, Any]
+    """run_options for the estimator. Currently, estimator's default run_options is not
+        included."""

qiskit/algorithms/gradients/finite_diff_estimator_gradient.py

@@ -19,8 +19,10 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
+from qiskit.exceptions import QiskitError
 from qiskit.opflow import PauliSumOp
 from qiskit.primitives import BaseEstimator
+from qiskit.providers import JobStatus
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .base_estimator_gradient import BaseEstimatorGradient
@@ -81,6 +83,9 @@ def _run(
             jobs.append(job)
 
         # combine the results
+        if any(job.status() is not JobStatus.DONE for job in jobs):
+            raise QiskitError("The gradient job was not completed successfully. ")
+
         results = [job.result() for job in jobs]
         gradients = []
         for result in results:

qiskit/algorithms/gradients/finite_diff_sampler_gradient.py

@@ -19,7 +19,9 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
+from qiskit.exceptions import QiskitError
 from qiskit.primitives import BaseSampler
+from qiskit.providers import JobStatus
 
 from .base_sampler_gradient import BaseSamplerGradient
 from .sampler_gradient_result import SamplerGradientResult
@@ -74,6 +76,8 @@ def _run(
             jobs.append(job)
 
         # combine the results
+        if any(job.status() is not JobStatus.DONE for job in jobs):
+            raise QiskitError("The gradient job was not completed successfully. ")
         results = [job.result() for job in jobs]
         gradients = []
         for i, result in enumerate(results):

qiskit/algorithms/gradients/lin_comb_estimator_gradient.py

@@ -20,14 +20,18 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
+from qiskit.exceptions import QiskitError
 from qiskit.opflow import PauliSumOp
 from qiskit.primitives import BaseEstimator
+from qiskit.primitives.utils import init_observable
+from qiskit.providers import JobStatus
 from qiskit.quantum_info import Pauli
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .base_estimator_gradient import BaseEstimatorGradient
 from .estimator_gradient_result import EstimatorGradientResult
-from .utils import make_lin_comb_gradient_circuit
+from .utils import _make_lin_comb_gradient_circuit
+
 
 Pauli_Z = Pauli("Z")
 
@@ -65,6 +69,8 @@ def _run(
         for circuit, observable, parameter_values_, parameters_ in zip(
             circuits, observables, parameter_values, parameters
         ):
+            # Make the observable as observable as :class:`~qiskit.quantum_info.SparsePauliOp`.
+            observables = init_observable(observable)
             # a set of parameters to be differentiated
             if parameters_ is None:
                 param_set = set(circuit.parameters)
@@ -76,7 +82,7 @@ def _run(
             observable_ = observable.expand(Pauli_Z)
             gradient_circuits_ = self._gradient_circuits.get(id(circuit))
             if gradient_circuits_ is None:
-                gradient_circuits_ = make_lin_comb_gradient_circuit(circuit)
+                gradient_circuits_ = _make_lin_comb_gradient_circuit(circuit)
                 self._gradient_circuits[id(circuit)] = gradient_circuits_
 
             # only compute the gradients for parameters in the parameter set
@@ -112,6 +118,8 @@ def _run(
             coeffs_all.append(coeffs)
 
         # combine the results
+        if any(job.status() is not JobStatus.DONE for job in jobs):
+            raise QiskitError("The gradient job was not completed successfully. ")
         results = [job.result() for job in jobs]
         gradients = []
         for i, result in enumerate(results):

qiskit/algorithms/gradients/lin_comb_sampler_gradient.py

@@ -20,11 +20,13 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
+from qiskit.exceptions import QiskitError
 from qiskit.primitives import BaseSampler
+from qiskit.providers import JobStatus
 
 from .base_sampler_gradient import BaseSamplerGradient
 from .sampler_gradient_result import SamplerGradientResult
-from .utils import make_lin_comb_gradient_circuit
+from .utils import _make_lin_comb_gradient_circuit
 
 
 class LinCombSamplerGradient(BaseSamplerGradient):
@@ -68,7 +70,7 @@ def _run(
             # TODO: support measurement in different basis (Y and Z+iY)
             gradient_circuits_ = self._gradient_circuits.get(id(circuit))
             if gradient_circuits_ is None:
-                gradient_circuits_ = make_lin_comb_gradient_circuit(circuit, add_measurement=True)
+                gradient_circuits_ = _make_lin_comb_gradient_circuit(circuit, add_measurement=True)
                 self._gradient_circuits[id(circuit)] = gradient_circuits_
 
             # only compute the gradients for parameters in the parameter set
@@ -101,6 +103,8 @@ def _run(
             coeffs_all.append(coeffs)
 
         # combine the results
+        if any(job.status() is not JobStatus.DONE for job in jobs):
+            raise QiskitError("The gradient job was not completed successfully. ")
         results = [job.result() for job in jobs]
         gradients = []
         for i, result in enumerate(results):

qiskit/algorithms/gradients/param_shift_estimator_gradient.py

@@ -20,14 +20,16 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
+from qiskit.exceptions import QiskitError
 from qiskit.opflow import PauliSumOp
 from qiskit.primitives import BaseEstimator
+from qiskit.providers import JobStatus
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .base_estimator_gradient import BaseEstimatorGradient
 from .estimator_gradient_result import EstimatorGradientResult
 
-from .utils import param_shift_preprocessing, make_param_shift_parameter_values
+from .utils import _param_shift_preprocessing, _make_param_shift_parameter_values
 
 
 class ParamShiftEstimatorGradient(BaseEstimatorGradient):
@@ -67,7 +69,7 @@ def _run(
             if self._gradient_circuits.get(id(circuit)):
                 gradient_circuit, base_parameter_values_all = self._gradient_circuits[id(circuit)]
             else:
-                gradient_circuit, base_parameter_values_all = param_shift_preprocessing(circuit)
+                gradient_circuit, base_parameter_values_all = _param_shift_preprocessing(circuit)
                 self._gradient_circuits[id(circuit)] = (
                     gradient_circuit,
                     base_parameter_values_all,
@@ -78,7 +80,7 @@ def _run(
                 gradient_parameter_values_minus,
                 result_indices,
                 coeffs,
-            ) = make_param_shift_parameter_values(
+            ) = _make_param_shift_parameter_values(
                 gradient_circuit_data=gradient_circuit,
                 base_parameter_values=base_parameter_values_all,
                 parameter_values=parameter_values_,
@@ -96,6 +98,8 @@ def _run(
             coeffs_all.append(coeffs)
 
         # combine the results
+        if any(job.status() is not JobStatus.DONE for job in jobs):
+            raise QiskitError("The gradient job was not completed successfully. ")
         results = [job.result() for job in jobs]
         gradients = []
         for i, result in enumerate(results):

qiskit/algorithms/gradients/param_shift_sampler_gradient.py

@@ -20,11 +20,13 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
+from qiskit.exceptions import QiskitError
 from qiskit.primitives import BaseSampler
+from qiskit.providers import JobStatus
 
 from .base_sampler_gradient import BaseSamplerGradient
 from .sampler_gradient_result import SamplerGradientResult
-from .utils import param_shift_preprocessing, make_param_shift_parameter_values
+from .utils import _param_shift_preprocessing, _make_param_shift_parameter_values
 
 
 class ParamShiftSamplerGradient(BaseSamplerGradient):
@@ -61,7 +63,7 @@ def _run(
             if self._gradient_circuits.get(id(circuit)):
                 gradient_circuit, base_parameter_values_all = self._gradient_circuits[id(circuit)]
             else:
-                gradient_circuit, base_parameter_values_all = param_shift_preprocessing(circuit)
+                gradient_circuit, base_parameter_values_all = _param_shift_preprocessing(circuit)
                 self._gradient_circuits[id(circuit)] = (
                     gradient_circuit,
                     base_parameter_values_all,
@@ -72,7 +74,7 @@ def _run(
                 gradient_parameter_values_minus,
                 result_indices,
                 coeffs,
-            ) = make_param_shift_parameter_values(
+            ) = _make_param_shift_parameter_values(
                 gradient_circuit_data=gradient_circuit,
                 base_parameter_values=base_parameter_values_all,
                 parameter_values=parameter_values_,
@@ -90,6 +92,8 @@ def _run(
             coeffs_all.append(coeffs)
 
         # combine the results
+        if any(job.status() is not JobStatus.DONE for job in jobs):
+            raise QiskitError("The gradient job was not completed successfully. ")
         results = [job.result() for job in jobs]
         gradients = []
         for i, result in enumerate(results):

qiskit/algorithms/gradients/sampler_gradient_result.py

@@ -18,20 +18,14 @@
 from typing import Any
 from dataclasses import dataclass
 
-from qiskit.result import QuasiDistribution
-
 
 @dataclass(frozen=True)
 class SamplerGradientResult:
-    """Result of ``SamplerGradient``.
-
-    Args:
-        gradients: The gradients of the quasi distributions.
-        metadata: Additional information about the job.
-        run_options: run_options for the sampler. Currently, sampler's default run_options is not
-        included.
-    """
+    """Result of SamplerGradient."""
 
-    gradients: list[list[QuasiDistribution]]
+    gradients: list[list[dict[int, float]]]
+    """The gradients of the sample probabilities."""
     metadata: list[dict[str, Any]]
+    """Additional information about the job."""
     run_options: dict[str, Any]
+    """run_options for the sampler. Currently, sampler's default run_options is not included"""

qiskit/algorithms/gradients/spsa_estimator_gradient.py

@@ -19,8 +19,10 @@
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
+from qiskit.exceptions import QiskitError
 from qiskit.opflow import PauliSumOp
 from qiskit.primitives import BaseEstimator
+from qiskit.providers import JobStatus
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .base_estimator_gradient import BaseEstimatorGradient
@@ -37,13 +39,15 @@ def __init__(
         self,
         estimator: BaseEstimator,
         epsilon: float,
+        batch_size: int = 1,
         seed: int | None = None,
         **run_options,
     ):
         """
         Args:
             estimator: The estimator used to compute the gradients.
             epsilon: The offset size for the SPSA gradients.
+            batch_size: The number of gradients to average.
             seed: The seed for a random perturbation vector.
             run_options: Backend runtime options used for circuit execution. The order of priority is:
                 run_options in ``run`` method > gradient's default run_options > primitive's default
@@ -55,6 +59,7 @@ def __init__(
         if epsilon <= 0:
             raise ValueError(f"epsilon ({epsilon}) should be positive.")
         self._epsilon = epsilon
+        self._batch_size = batch_size
         self._seed = np.random.default_rng(seed) if seed else np.random.default_rng()
 
         super().__init__(estimator, **run_options)
@@ -79,20 +84,35 @@ def _run(
                 indices = [circuit.parameters.data.index(p) for p in parameters_]
             metadata_.append({"parameters": [circuit.parameters[idx] for idx in indices]})
 
-            offset = (-1) ** (self._seed.integers(0, 2, len(circuit.parameters)))
-            plus = parameter_values_ + self._epsilon * offset
-            minus = parameter_values_ - self._epsilon * offset
+            offset = [
+                (-1) ** (self._seed.integers(0, 2, len(circuit.parameters)))
+                for _ in range(self._batch_size)
+            ]
+
+            plus = [parameter_values_ + self._epsilon * offset_ for offset_ in offset]
+            minus = [parameter_values_ - self._epsilon * offset_ for offset_ in offset]
             offsets.append(offset)
 
-            job = self._estimator.run([circuit] * 2, [observable] * 2, [plus, minus], **run_options)
+            job = self._estimator.run(
+                [circuit] * 2 * self._batch_size,
+                [observable] * 2 * self._batch_size,
+                plus + minus,
+                **run_options,
+            )
             jobs.append(job)
 
         # combine the results
+        if any(job.status() is not JobStatus.DONE for job in jobs):
+            raise QiskitError("The gradient job was not completed successfully. ")
         results = [job.result() for job in jobs]
         gradients = []
         for i, result in enumerate(results):
             n = len(result.values) // 2  # is always a multiple of 2
-            gradient = (result.values[:n] - result.values[n:]) / (2 * self._epsilon * offsets[i])
+            gradient_ = (result.values[:n] - result.values[n:]) / (2 * self._epsilon)
+            # take the average of the spsa gradients
+            gradient = np.average(
+                np.array([grad / offset for grad, offset in zip(gradient_, offsets[i])]), axis=0
+            )
             indices = [circuits[i].parameters.data.index(p) for p in metadata_[i]["parameters"]]
             gradients.append(gradient[indices])