Support for following VQE minimization in VQESolver

tangelo/algorithms/variational/tests/test_vqe_solver.py

@@ -655,6 +655,28 @@ def test_rotosolve_unsupported(self):
         with self.assertRaises(ValueError):
             VQESolver(options).build()
 
+    def test_save_energies(self):
+        """Performing a deterministic number of optimization steps (calls to
+        energy_estimation). The energies attributes should have n elements,
+        where n is the number of optimization steps.
+        """
+
+        vqe_options = {"molecule": mol_H2_sto3g,
+                       "ansatz": Circuit([Gate("X", 0), Gate("X", 1)], n_qubits=4),
+                       "qubit_mapping": "JW",
+                       "up_then_down": False,
+                       "save_energies": True,
+                       "verbose": False}
+        vqe_solver = VQESolver(vqe_options)
+        vqe_solver.build()
+
+        n_steps = 3
+        # Deterministic number of calls to energy_estimation.
+        for _ in range(n_steps):
+            vqe_solver.energy_estimation(var_params=[])
+
+        self.assertEqual(len(vqe_solver.energies), n_steps)
+
 
 if __name__ == "__main__":
     unittest.main()

tangelo/algorithms/variational/vqe_solver.py

@@ -16,6 +16,7 @@
 electronic structure calculations.
 """
 
+from contextlib import nullcontext
 import warnings
 import itertools
 from typing import Optional, Union, List
@@ -91,6 +92,7 @@ class VQESolver:
         ref_state (array or Circuit): The reference configuration to use. Replaces HF state
             QMF, QCC, ILC require ref_state to be an array. UCC1, UCC3, VSQS can not use a
             different ref_state than HF by construction.
+        save_energies (bool): Flag for saving energy estimation values.
     """
 
     def __init__(self, opt_dict):
@@ -114,6 +116,7 @@ def __init__(self, opt_dict):
         self.verbose: bool = copt_dict.pop("verbose", False)
         self.projective_circuit: Circuit = copt_dict.pop("projective_circuit", None)
         self.ref_state: Optional[Union[list, Circuit]] = copt_dict.pop("ref_state", None)
+        self.save_energies: bool = copt_dict.pop("save_energies", False)
 
         if len(copt_dict) > 0:
             raise KeyError(f"The following keywords are not supported in {self.__class__.__name__}: \n {copt_dict.keys()}")
@@ -165,6 +168,8 @@ def __init__(self, opt_dict):
         self.optimal_var_params = None
         self.builtin_ansatze = set(BuiltInAnsatze)
 
+        self.energies = list()
+
     def build(self):
         """Build the underlying objects required to run the VQE algorithm
         afterwards.
@@ -311,6 +316,9 @@ def energy_estimation(self, var_params):
         if self.verbose:
             print(f"\tEnergy = {energy:.7f} ")
 
+        if self.save_energies:
+            self.energies += [energy]
+
         return energy
 
     def operator_expectation(self, operator, var_params=None, n_active_mos=None, n_active_electrons=None, n_active_sos=None, spin=None, ref_state=Circuit()):
@@ -668,7 +676,7 @@ def _default_optimizer(self, func, var_params):
 
         from scipy.optimize import minimize
 
-        with HiddenPrints():
+        with HiddenPrints() if not self.verbose else nullcontext():
             result = minimize(func, var_params, method="SLSQP",
                               options={"disp": True, "maxiter": 2000, "eps": 1e-5, "ftol": 1e-5})