Allow Qiskit Optimizers in the runtime programs

qiskit_optimization/runtime/qaoa_program.py

@@ -18,6 +18,7 @@
 
 from qiskit import QuantumCircuit
 from qiskit.algorithms import MinimumEigensolverResult
+from qiskit.algorithms.optimizers import Optimizer
 from qiskit.circuit.library import QAOAAnsatz
 from qiskit.opflow import OperatorBase
 from qiskit.providers import Provider
@@ -32,7 +33,7 @@ class QAOAProgram(VQEProgram):
 
     def __init__(
         self,
-        optimizer: Optional[Dict[str, Any]] = None,
+        optimizer: Optional[Union[Optimizer, Dict[str, Any]]] = None,
         reps: int = 1,
         initial_state: Optional[QuantumCircuit] = None,
         mixer: Union[QuantumCircuit, OperatorBase] = None,
@@ -46,11 +47,11 @@ def __init__(
     ) -> None:
         """
         Args:
-            optimizer: A dictionary specifying a classical optimizer.
-                Currently only SPSA and QN-SPSA are supported. Per default, SPSA is used.
-                The dictionary must contain a key ``name`` for the name of the optimizer and may
-                contain additional keys for the settings.
-                E.g. ``{'name': 'SPSA', 'maxiter': 100}``.
+            optimizer: An optimizer or dictionary specifying a classical optimizer.
+                If a dictionary, only SPSA and QN-SPSA are supported. The dictionary must contain a
+                key ``name`` for the name of the optimizer and may contain additional keys for the
+                settings. E.g. ``{'name': 'SPSA', 'maxiter': 100}``.
+                Per default, SPSA is used.
             reps: the integer parameter :math:`p` as specified in https://arxiv.org/abs/1411.4028,
                 Has a minimum valid value of 1.
             initial_state: An optional initial state to prepend the QAOA circuit with

qiskit_optimization/runtime/vqe_program.py

@@ -13,14 +13,15 @@
 """The Qiskit Optimization VQE Quantum Program."""
 
 
-from typing import List, Callable, Optional, Any, Dict
+from typing import List, Callable, Optional, Any, Dict, Union
 import numpy as np
 
 from qiskit import QuantumCircuit
 from qiskit.exceptions import QiskitError
 from qiskit.providers import Provider
 from qiskit.providers.backend import Backend
 from qiskit.algorithms import MinimumEigensolver, MinimumEigensolverResult, VQEResult
+from qiskit.algorithms.optimizers import Optimizer, SPSA
 from qiskit.opflow import OperatorBase, PauliSumOp
 from qiskit.quantum_info import SparsePauliOp
 
@@ -35,7 +36,7 @@ class VQEProgram(MinimumEigensolver):
     def __init__(
         self,
         ansatz: QuantumCircuit,
-        optimizer: Optional[Dict[str, Any]] = None,
+        optimizer: Optional[Union[Optimizer, Dict[str, Any]]] = None,
         initial_point: Optional[np.ndarray] = None,
         provider: Optional[Provider] = None,
         backend: Optional[Backend] = None,
@@ -47,11 +48,11 @@ def __init__(
         """
         Args:
             ansatz: A parameterized circuit used as Ansatz for the wave function.
-            optimizer: A dictionary specifying a classical optimizer.
-                Currently only SPSA and QN-SPSA are supported. Per default, SPSA is used.
-                The dictionary must contain a key ``name`` for the name of the optimizer and may
-                contain additional keys for the settings.
-                E.g. ``{'name': 'SPSA', 'maxiter': 100}``.
+            optimizer: An optimizer or dictionary specifying a classical optimizer.
+                If a dictionary, only SPSA and QN-SPSA are supported. The dictionary must contain a
+                key ``name`` for the name of the optimizer and may contain additional keys for the
+                settings. E.g. ``{'name': 'SPSA', 'maxiter': 100}``.
+                Per default, SPSA is used.
             backend: The backend to run the circuits on.
             initial_point: An optional initial point (i.e. initial parameter values)
                 for the optimizer. If ``None`` a random vector is used.
@@ -66,7 +67,7 @@ def __init__(
                 steps. Per default False.
         """
         if optimizer is None:
-            optimizer = {"name": "SPSA"}
+            optimizer = SPSA(maxiter=300)
 
         # define program name
         self._program_id = "vqe"
@@ -120,21 +121,25 @@ def ansatz(self, ansatz: QuantumCircuit) -> None:
         self._ansatz = ansatz
 
     @property
-    def optimizer(self) -> Dict[str, Any]:
+    def optimizer(self) -> Union[Optimizer, Dict[str, Any]]:
         """Return the dictionary describing the optimizer."""
         return self._optimizer
 
     @optimizer.setter
-    def optimizer(self, settings: Dict[str, Any]) -> None:
+    def optimizer(self, optimizer: Union[Optimizer, Dict[str, Any]]) -> None:
         """Set the optimizer."""
-        if "name" not in settings.keys():
-            raise ValueError(
-                "The settings must contain a ``name`` key specifying the type of " "the optimizer."
-            )
+        if isinstance(optimizer, Optimizer):
+            self._optimizer = optimizer
+        else:
+            if "name" not in optimizer.keys():
+                raise ValueError(
+                    "The optimizer dictionary must contain a ``name`` key specifying the type "
+                    "of the optimizer."
+                )
 
-        _validate_optimizer_settings(settings)
+            _validate_optimizer_settings(optimizer)
 
-        self._optimizer = settings
+            self._optimizer = optimizer
 
     @property
     def backend(self) -> Optional[Backend]:
@@ -218,9 +223,7 @@ def wrapped_callback(*args):
             return None
 
     def compute_minimum_eigenvalue(
-        self,
-        operator: OperatorBase,
-        aux_operators: Optional[List[Optional[OperatorBase]]] = None,
+        self, operator: OperatorBase, aux_operators: Optional[List[Optional[OperatorBase]]] = None
     ) -> MinimumEigensolverResult:
         """Calls the VQE Runtime to approximate the ground state of the given operator.
 
@@ -280,11 +283,6 @@ def compute_minimum_eigenvalue(
             raise RuntimeError(f"The job {job.job_id()} failed unexpectedly.") from exc
 
         # re-build result from serialized return value
-        vqe_result = self._parse_job_result(job, result)
-        return vqe_result
-
-    def _parse_job_result(self, job, result):
-        """Build the VQE result object from the job result."""
         vqe_result = VQEProgramResult()
         vqe_result.job_id = job.job_id()
         vqe_result.cost_function_evals = result.get("cost_function_evals", None)
@@ -297,6 +295,7 @@ def _parse_job_result(self, job, result):
         vqe_result.optimizer_evals = result.get("optimizer_evals", None)
         vqe_result.optimizer_time = result.get("optimizer_time", None)
         vqe_result.optimizer_history = result.get("optimizer_history", None)
+
         return vqe_result
 
 
@@ -309,8 +308,8 @@ class VQEProgramResult(VQEResult):
 
     def __init__(self) -> None:
         super().__init__()
-        self._job_id: Optional[str] = None
-        self._optimizer_history: Optional[Dict[str, Any]] = None
+        self._job_id = None  # type: str
+        self._optimizer_history = None  # type: Dict[str, Any]
 
     @property
     def job_id(self) -> str:

releasenotes/notes/runtime-optimizers-58f30311f969f54f.yaml

@@ -0,0 +1,7 @@
+---
+fixes:
+  - |
+    Allow Qiskit's :class:`~qiskit.algorithms.optimizers.Optimizer` classes as input for
+    the ``optimizer`` in the :class:`~qiskit_optimization.runtime.VQEProgram` and 
+    :class:`~qiskit_optimization.runtime.QAOAProgram` instead of only
+    dictionaries.

test/runtime/fake_vqeruntime.py

@@ -14,6 +14,7 @@
 
 from typing import Dict, Any
 import numpy as np
+from qiskit.algorithms.optimizers import Optimizer
 from qiskit.circuit import QuantumCircuit
 from qiskit.opflow import PauliSumOp
 from qiskit.providers import Provider
@@ -59,7 +60,7 @@ def run(self, program_id, inputs, options, callback=None):
             "aux_operators": (list, type(None)),
             "ansatz": QuantumCircuit,
             "initial_point": (np.ndarray, str),
-            "optimizer": dict,
+            "optimizer": (Optimizer, dict),
             "shots": int,
             "measurement_error_mitigation": bool,
             "store_intermediate": bool,

test/runtime/test_qaoaprogram.py

@@ -15,7 +15,9 @@
 from test import QiskitOptimizationTestCase
 
 import unittest
+from ddt import ddt, data
 import numpy as np
+from qiskit.algorithms.optimizers import COBYLA
 from qiskit.providers.basicaer import QasmSimulatorPy
 from qiskit.opflow import I, Z
 
@@ -24,19 +26,23 @@
 from .fake_vqeruntime import FakeRuntimeProvider
 
 
+@ddt
 class TestQAOAProgram(QiskitOptimizationTestCase):
     """Test the QAOA program."""
 
     def setUp(self):
         super().setUp()
         self.provider = FakeRuntimeProvider()
 
-    def test_standard_case(self):
+    @data(
+        {"name": "SPSA", "maxiter": 100},
+        COBYLA(),
+    )
+    def test_standard_case(self, optimizer):
         """Test a standard use case."""
         operator = Z ^ I ^ Z
         reps = 2
         initial_point = np.random.RandomState(42).random(2 * reps)
-        optimizer = {"name": "SPSA", "maxiter": 100}
         backend = QasmSimulatorPy()
 
         qaoa = QAOAProgram(

test/runtime/test_vqeprogram.py

@@ -15,7 +15,9 @@
 from test import QiskitOptimizationTestCase
 
 import unittest
+from ddt import ddt, data
 import numpy as np
+from qiskit.algorithms.optimizers import COBYLA
 from qiskit.providers.basicaer import QasmSimulatorPy
 from qiskit.circuit.library import RealAmplitudes
 from qiskit.opflow import I, Z
@@ -25,19 +27,23 @@
 from .fake_vqeruntime import FakeRuntimeProvider
 
 
+@ddt
 class TestVQEProgram(QiskitOptimizationTestCase):
     """Test the VQE program."""
 
     def setUp(self):
         super().setUp()
         self.provider = FakeRuntimeProvider()
 
-    def test_standard_case(self):
+    @data(
+        {"name": "SPSA", "maxiter": 100},
+        COBYLA(),
+    )
+    def test_standard_case(self, optimizer):
         """Test a standard use case."""
         circuit = RealAmplitudes(3)
         operator = Z ^ I ^ Z
         initial_point = np.random.RandomState(42).random(circuit.num_parameters)
-        optimizer = {"name": "SPSA", "maxiter": 100}
         backend = QasmSimulatorPy()
 
         vqe = VQEProgram(