QFI with the primitives (#8688)

* wip qfi

* wip qfi

* updated

* added tests

* updated

* updated

* updated

* lin comb qfi

* fix

* finilizing

* fix lint

* started working on block diag qfi

* updated utils

* fix

* fix options

* fix

* lint

* fix

* fix list

* remove estimator from base qfi

* fix

* make a directory for gradient tests

* added option to baseqfi + fied shots behavior

* fixed qfi's derivative type complex

* fix

* fix

* make parameter_values back to float

* updated

* fix lint

* updated

* updated

Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

qiskit/algorithms/gradients/__init__.py

@@ -17,21 +17,15 @@
 
 .. currentmodule:: qiskit.algorithms.gradients
 
-Base Classes
-============
-
-.. autosummary::
-   :toctree: ../stubs/
-
-   BaseSamplerGradient
-   BaseEstimatorGradient
 
 Estimator Gradients
 ===================
 
 .. autosummary::
    :toctree: ../stubs/
 
+   BaseEstimatorGradient
+   DerivativeType
    FiniteDiffEstimatorGradient
    LinCombEstimatorGradient
    ParamShiftEstimatorGradient
@@ -43,44 +37,61 @@
 .. autosummary::
    :toctree: ../stubs/
 
+   BaseSamplerGradient
    FiniteDiffSamplerGradient
    LinCombSamplerGradient
    ParamShiftSamplerGradient
    SPSASamplerGradient
 
+QFI
+===
+.. autosummary::
+   :toctree: ../stubs/
+
+   BaseQFI
+   LinCombQFI
+
 Results
 =======
 
 .. autosummary::
    :toctree: ../stubs/
 
    EstimatorGradientResult
+   QFIResult
    SamplerGradientResult
 """
 
 from .base_estimator_gradient import BaseEstimatorGradient
+from .base_qfi import BaseQFI
 from .base_sampler_gradient import BaseSamplerGradient
 from .estimator_gradient_result import EstimatorGradientResult
 from .finite_diff_estimator_gradient import FiniteDiffEstimatorGradient
 from .finite_diff_sampler_gradient import FiniteDiffSamplerGradient
-from .lin_comb_estimator_gradient import LinCombEstimatorGradient
+from .lin_comb_estimator_gradient import DerivativeType, LinCombEstimatorGradient
+from .lin_comb_qfi import LinCombQFI
 from .lin_comb_sampler_gradient import LinCombSamplerGradient
 from .param_shift_estimator_gradient import ParamShiftEstimatorGradient
 from .param_shift_sampler_gradient import ParamShiftSamplerGradient
+from .qfi_result import QFIResult
 from .sampler_gradient_result import SamplerGradientResult
 from .spsa_estimator_gradient import SPSAEstimatorGradient
 from .spsa_sampler_gradient import SPSASamplerGradient
 
 __all__ = [
     "BaseEstimatorGradient",
+    "BaseQFI",
     "BaseSamplerGradient",
+    "DerivativeType",
     "EstimatorGradientResult",
     "FiniteDiffEstimatorGradient",
     "FiniteDiffSamplerGradient",
     "LinCombEstimatorGradient",
+    "LinCombQFI",
     "LinCombSamplerGradient",
     "ParamShiftEstimatorGradient",
     "ParamShiftSamplerGradient",
+    "QFIResult",
     "SamplerGradientResult",
     "SPSAEstimatorGradient",
     "SPSASamplerGradient",

qiskit/algorithms/gradients/base_estimator_gradient.py

@@ -131,7 +131,6 @@ def _validate_arguments(
         Raises:
             ValueError: Invalid arguments are given.
         """
-        # Validation
         if len(circuits) != len(parameter_values):
             raise ValueError(
                 f"The number of circuits ({len(circuits)}) does not match "

qiskit/algorithms/gradients/base_qfi.py

@@ -0,0 +1,167 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2022.
+#
+# 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
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+Abstract base class of the Quantum Fisher Information (QFI).
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+from copy import copy
+
+from qiskit.circuit import Parameter, QuantumCircuit
+from qiskit.algorithms import AlgorithmJob
+from qiskit.providers import Options
+
+from .qfi_result import QFIResult
+
+
+class BaseQFI(ABC):
+    r"""Base class to computes the Quantum Fisher Information (QFI) given a pure,
+    parameterized quantum state. QFI is defined as:
+
+    .. math::
+
+        \mathrm{QFI}_{kl}= 4 \mathrm{Re}[\langle \partial_k \psi | \partial_l \psi \rangle
+            - \langle\partial_k \psi | \psi \rangle \langle\psi | \partial_l \psi \rangle].
+    """
+
+    def __init__(
+        self,
+        options: Options | None = None,
+    ):
+        """
+        Args:
+            options: Backend runtime options used for circuit execution. The order of priority is:
+                options in ``run`` method > QFI's default options > primitive's default
+                setting. Higher priority setting overrides lower priority setting.
+        """
+        self._default_options = Options()
+        if options is not None:
+            self._default_options.update_options(**options)
+
+    def run(
+        self,
+        circuits: Sequence[QuantumCircuit],
+        parameter_values: Sequence[Sequence[float]],
+        parameters: Sequence[Sequence[Parameter] | None] | None = None,
+        **options,
+    ) -> AlgorithmJob:
+        """Run the job of the QFIs on the given circuits.
+
+        Args:
+            circuits: The list of quantum circuits to compute the QFIs.
+            parameter_values: The list of parameter values to be bound to the circuit.
+            parameters: The sequence of parameters to calculate only the QFIs of
+                the specified parameters. Each sequence of parameters corresponds to a circuit in
+                ``circuits``. Defaults to None, which means that the QFIs of all parameters in
+                each circuit are calculated.
+            options: Primitive backend runtime options used for circuit execution.
+                The order of priority is: options in ``run`` method > QFI's
+                default options > primitive's default setting.
+                Higher priority setting overrides lower priority setting
+
+        Returns:
+            The job object of the QFIs of the expectation values. The i-th result corresponds to
+            ``circuits[i]`` evaluated with parameters bound as ``parameter_values[i]``. The j-th
+            element of the i-th result corresponds to the QFI of the i-th circuit with respect
+            to the j-th parameter.
+
+        Raises:
+            ValueError: Invalid arguments are given.
+        """
+        # if ``parameters`` is none, all parameters in each circuit are differentiated.
+        if parameters is None:
+            parameters = [None] * len(circuits)
+        # Validate the arguments.
+        self._validate_arguments(circuits, parameter_values, parameters)
+        # The priority of run option is as follows:
+        # options in ``run`` method > QFI's default options > primitive's default setting.
+        opts = copy(self._default_options)
+        opts.update_options(**options)
+        job = AlgorithmJob(self._run, circuits, parameter_values, parameters, **opts.__dict__)
+        job.submit()
+        return job
+
+    @abstractmethod
+    def _run(
+        self,
+        circuits: Sequence[QuantumCircuit],
+        parameter_values: Sequence[Sequence[float]],
+        parameters: Sequence[Sequence[Parameter] | None],
+        **options,
+    ) -> QFIResult:
+        """Compute the QFIs on the given circuits."""
+        raise NotImplementedError()
+
+    def _validate_arguments(
+        self,
+        circuits: Sequence[QuantumCircuit],
+        parameter_values: Sequence[Sequence[float]],
+        parameters: Sequence[Sequence[Parameter] | None] | None = None,
+    ) -> None:
+        """Validate the arguments of the ``run`` method.
+
+        Args:
+            circuits: The list of quantum circuits to compute the QFIs.
+            parameter_values: The list of parameter values to be bound to the circuit.
+            parameters: The Sequence of Sequence of Parameters to calculate only the QFIs of
+                the specified parameters. Each Sequence of Parameters corresponds to a circuit in
+                ``circuits``. Defaults to None, which means that the QFIs of all parameters in
+                each circuit are calculated.
+
+        Raises:
+            ValueError: Invalid arguments are given.
+        """
+        if len(circuits) != len(parameter_values):
+            raise ValueError(
+                f"The number of circuits ({len(circuits)}) does not match "
+                f"the number of parameter value sets ({len(parameter_values)})."
+            )
+
+        if parameters is not None:
+            if len(circuits) != len(parameters):
+                raise ValueError(
+                    f"The number of circuits ({len(circuits)}) does not match "
+                    f"the number of the specified parameter sets ({len(parameters)})."
+                )
+
+        for i, (circuit, parameter_value) in enumerate(zip(circuits, parameter_values)):
+            if not circuit.num_parameters:
+                raise ValueError(f"The {i}-th circuit is not parameterised.")
+            if len(parameter_value) != circuit.num_parameters:
+                raise ValueError(
+                    f"The number of values ({len(parameter_value)}) does not match "
+                    f"the number of parameters ({circuit.num_parameters}) for the {i}-th circuit."
+                )
+
+    @property
+    @abstractmethod
+    def options(self) -> Options:
+        """Return the union of estimator options setting and QFI default options,
+        where, if the same field is set in both, the QFI's default options override
+        the primitive's default setting.
+
+        Returns:
+            The QFI default + estimator options.
+        """
+        pass
+
+    def update_default_options(self, **options):
+        """Update the QFI's default options setting.
+
+        Args:
+            **options: The fields to update the default options.
+        """
+        self._default_options.update_options(**options)

qiskit/algorithms/gradients/base_sampler_gradient.py

@@ -116,7 +116,6 @@ def _validate_arguments(
         Raises:
             ValueError: Invalid arguments are given.
         """
-        # Validate the arguments.
         if len(circuits) != len(parameter_values):
             raise ValueError(
                 f"The number of circuits ({len(circuits)}) does not match "

qiskit/algorithms/gradients/finite_diff_estimator_gradient.py

@@ -22,6 +22,7 @@
 from qiskit.circuit import Parameter, QuantumCircuit
 from qiskit.opflow import PauliSumOp
 from qiskit.primitives import BaseEstimator
+from qiskit.providers import Options
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .base_estimator_gradient import BaseEstimatorGradient
@@ -33,7 +34,7 @@ class FiniteDiffEstimatorGradient(BaseEstimatorGradient):
     Compute the gradients of the expectation values by finite difference method.
     """
 
-    def __init__(self, estimator: BaseEstimator, epsilon: float, **options):
+    def __init__(self, estimator: BaseEstimator, epsilon: float, options: Options | None = None):
         """
         Args:
             estimator: The estimator used to compute the gradients.
@@ -50,7 +51,7 @@ def __init__(self, estimator: BaseEstimator, epsilon: float, **options):
             raise ValueError(f"epsilon ({epsilon}) should be positive.")
         self._epsilon = epsilon
         self._base_parameter_values_dict = {}
-        super().__init__(estimator, **options)
+        super().__init__(estimator, options)
 
     def _run(
         self,

qiskit/algorithms/gradients/finite_diff_sampler_gradient.py

@@ -21,6 +21,7 @@
 from qiskit.algorithms import AlgorithmError
 from qiskit.circuit import Parameter, QuantumCircuit
 from qiskit.primitives import BaseSampler
+from qiskit.providers import Options
 
 from .base_sampler_gradient import BaseSamplerGradient
 from .sampler_gradient_result import SamplerGradientResult
@@ -33,7 +34,7 @@ def __init__(
         self,
         sampler: BaseSampler,
         epsilon: float,
-        **options,
+        options: Options | None = None,
     ):
         """
         Args:
@@ -50,7 +51,7 @@ def __init__(
         if epsilon <= 0:
             raise ValueError(f"epsilon ({epsilon}) should be positive.")
         self._epsilon = epsilon
-        super().__init__(sampler, **options)
+        super().__init__(sampler, options)
 
     def _run(
         self,