Merge e88f7de into 2e1ba88

qiskit/aqua/algorithms/adaptive/vqe/vqe.py

@@ -21,6 +21,7 @@
 from typing import Optional, List, Callable
 import logging
 import functools
+from time import time
 
 import numpy as np
 from qiskit import ClassicalRegister, QuantumCircuit
@@ -333,20 +334,27 @@ def _build_parameterized_circuits():
                 circuits.append(circuit)
             to_be_simulated_circuits = functools.reduce(lambda x, y: x + y, circuits)
 
+        start_time = time()
         result = self._quantum_instance.execute(to_be_simulated_circuits,
                                                 self._parameterized_circuits is not None)
+        end_time = time()
+        logger.info('Circuits execution - %.5f (ms)',
+                    (end_time - start_time) * 1000)
 
         for idx, _ in enumerate(parameter_sets):
+            start_time = time()
             mean, std = self._operator.evaluate_with_result(
                 result=result, statevector_mode=self._quantum_instance.is_statevector,
                 use_simulator_snapshot_mode=self._use_simulator_snapshot_mode,
                 circuit_name_prefix=str(idx))
+            end_time = time()
             mean_energy.append(np.real(mean))
             std_energy.append(np.real(std))
             self._eval_count += 1
             if self._callback is not None:
                 self._callback(self._eval_count, parameter_sets[idx], np.real(mean), np.real(std))
-            logger.info('Energy evaluation %s returned %s', self._eval_count, np.real(mean))
+            logger.info('Energy evaluation %s returned %s - %.5f (ms)',
+                        self._eval_count, np.real(mean), (end_time - start_time) * 1000)
 
         return mean_energy if len(mean_energy) > 1 else mean_energy[0]