Fix VQE result, add ansatz (Qiskit/qiskit#8816)

* Add ansatz to result

* Add ansatz to eigen. results

* Update qiskit/algorithms/minimum_eigensolvers/minimum_eigensolver.py

Co-authored-by: Julien Gacon <gaconju@gmail.com>

* Copy ansatz

* Apply comments

* Change name to optimal_circuit(s)

* add blank line

* move optimal circuit to variational result

* remove import

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

qiskit_algorithms/eigensolvers/vqd.py

@@ -259,7 +259,7 @@ def compute_eigenvalues(
 
             eval_time = time() - start_time
 
-            self._update_vqd_result(result, opt_result, eval_time, self.ansatz)
+            self._update_vqd_result(result, opt_result, eval_time, self.ansatz.copy())
 
             if aux_operators is not None:
                 aux_value = estimate_observables(
@@ -388,6 +388,7 @@ def _build_vqd_result() -> VQDResult:
         result.optimizer_times = []
         result.eigenvalues = []
         result.optimizer_results = []
+        result.optimal_circuits = []
         return result
 
     @staticmethod
@@ -400,6 +401,7 @@ def _update_vqd_result(result, opt_result, eval_time, ansatz) -> VQDResult:
         result.optimizer_times.append(eval_time)
         result.eigenvalues.append(opt_result.fun + 0j)
         result.optimizer_results.append(opt_result)
+        result.optimal_circuits.append(ansatz)
         return result
 
 
@@ -414,6 +416,7 @@ def __init__(self) -> None:
         self._optimal_points = None
         self._optimal_parameters = None
         self._optimizer_results = None
+        self._optimal_circuits = None
 
     @property
     def cost_function_evals(self) -> Sequence[int] | None:
@@ -474,3 +477,13 @@ def optimizer_results(self) -> Sequence[OptimizerResult] | None:
     def optimizer_results(self, value: Sequence[OptimizerResult]) -> None:
         """Sets optimizer results"""
         self._optimizer_results = value
+
+    @property
+    def optimal_circuits(self) -> list[QuantumCircuit]:
+        """The optimal circuits. Along with the optimal parameters,
+        these can be used to retrieve the different eigenstates."""
+        return self._optimal_circuits
+
+    @optimal_circuits.setter
+    def optimal_circuits(self, optimal_circuits: list[QuantumCircuit]) -> None:
+        self._optimal_circuits = optimal_circuits

qiskit_algorithms/minimum_eigensolvers/sampling_vqe.py

@@ -234,7 +234,12 @@ def compute_minimum_eigenvalue(
             aux_operators_evaluated = None
 
         return self._build_sampling_vqe_result(
-            optimizer_result, aux_operators_evaluated, best_measurement, final_state, optimizer_time
+            self.ansatz.copy(),
+            optimizer_result,
+            aux_operators_evaluated,
+            best_measurement,
+            final_state,
+            optimizer_time,
         )
 
     def _get_evaluate_energy(
@@ -306,6 +311,7 @@ def evaluate_energy(parameters):
 
     def _build_sampling_vqe_result(
         self,
+        ansatz: QuantumCircuit,
         optimizer_result: OptimizerResult,
         aux_operators_evaluated: ListOrDict[tuple[complex, tuple[complex, int]]],
         best_measurement: dict[str, Any],
@@ -323,6 +329,7 @@ def _build_sampling_vqe_result(
         result.optimizer_result = optimizer_result
         result.best_measurement = best_measurement["best"]
         result.eigenstate = final_state
+        result.optimal_circuit = ansatz
         return result
 
 

qiskit_algorithms/minimum_eigensolvers/vqe.py

@@ -200,7 +200,9 @@ def compute_minimum_eigenvalue(
         else:
             aux_operators_evaluated = None
 
-        return self._build_vqe_result(optimizer_result, aux_operators_evaluated, optimizer_time)
+        return self._build_vqe_result(
+            self.ansatz, optimizer_result, aux_operators_evaluated, optimizer_time
+        )
 
     @classmethod
     def supports_aux_operators(cls) -> bool:
@@ -308,11 +310,13 @@ def _check_operator_ansatz(self, operator: BaseOperator | PauliSumOp):
 
     def _build_vqe_result(
         self,
+        ansatz: QuantumCircuit,
         optimizer_result: OptimizerResult,
         aux_operators_evaluated: ListOrDict[tuple[complex, tuple[complex, int]]],
         optimizer_time: float,
     ) -> VQEResult:
         result = VQEResult()
+        result.optimal_circuit = ansatz.copy()
         result.eigenvalue = optimizer_result.fun
         result.cost_function_evals = optimizer_result.nfev
         result.optimal_point = optimizer_result.x

qiskit_algorithms/variational_algorithm.py

@@ -30,6 +30,8 @@
 from abc import ABC, abstractmethod
 import numpy as np
 
+from qiskit.circuit import QuantumCircuit
+
 from .algorithm_result import AlgorithmResult
 from .optimizers import OptimizerResult
 
@@ -61,6 +63,7 @@ def __init__(self) -> None:
         self._optimal_point = None
         self._optimal_parameters = None
         self._optimizer_result = None
+        self._optimal_circuit = None
 
     @property
     def optimizer_evals(self) -> Optional[int]:
@@ -121,3 +124,14 @@ def optimizer_result(self) -> Optional[OptimizerResult]:
     def optimizer_result(self, value: OptimizerResult) -> None:
         """Sets optimizer result"""
         self._optimizer_result = value
+
+    @property
+    def optimal_circuit(self) -> QuantumCircuit:
+        """The optimal circuits. Along with the optimal parameters,
+        these can be used to retrieve the minimum eigenstate.
+        """
+        return self._optimal_circuit
+
+    @optimal_circuit.setter
+    def optimal_circuit(self, optimal_circuit: QuantumCircuit) -> None:
+        self._optimal_circuit = optimal_circuit

test/eigensolvers/test_vqd.py

@@ -100,6 +100,14 @@ def test_basic_operator(self, op):
         with self.subTest(msg="assert optimizer_times is set"):
             self.assertIsNotNone(result.optimizer_times)
 
+        with self.subTest(msg="assert return ansatz is set"):
+            job = self.estimator.run(
+                result.optimal_circuits,
+                [op] * len(result.optimal_points),
+                result.optimal_points,
+            )
+            np.testing.assert_array_almost_equal(job.result().values, result.eigenvalues, 6)
+
     @data(H2_PAULI, H2_OP)
     def test_mismatching_num_qubits(self, op):
         """Ensuring circuit and operator mismatch is caught"""

test/minimum_eigensolvers/test_vqe.py

@@ -107,6 +107,11 @@ def test_basic_aer_statevector(self, estimator):
         with self.subTest(msg="assert optimizer_result."):
             self.assertAlmostEqual(result.optimizer_result.fun, self.h2_energy, places=5)
 
+        with self.subTest(msg="assert return ansatz is set"):
+            estimator = Estimator()
+            job = estimator.run(result.optimal_circuit, self.h2_op, result.optimal_point)
+            np.testing.assert_array_almost_equal(job.result().values, result.eigenvalue, 6)
+
     def test_invalid_initial_point(self):
         """Test the proper error is raised when the initial point has the wrong size."""
         ansatz = self.ryrz_wavefunction