def _compute_std_devs( observables_expect_sampled: OperatorBase, observables: ListOrDict[OperatorBase], expectation: ExpectationBase, quantum_instance: Union[QuantumInstance, BaseBackend, Backend], ) -> List[complex]: """ Calculates a list of standard deviations from expectation values of observables provided. Args: observables_expect_sampled: Expected values of observables. observables: A list or a dictionary of operators whose expectation values are to be calculated. expectation: An instance of ExpectationBase which defines a method for calculating expectation values. quantum_instance: A quantum instance used for calculations. Returns: A list of standard deviations. """ variances = np.real(expectation.compute_variance(observables_expect_sampled)) if not isinstance(variances, np.ndarray) and variances == 0.0: # when `variances` is a single value equal to 0., our expectation value is exact and we # manually ensure the variances to be a list of the correct length variances = np.zeros(len(observables), dtype=float) std_devs = np.sqrt(variances / quantum_instance.run_config.shots) return std_devs
def _eval_aux_ops( self, parameters: np.ndarray, aux_operators: ListOrDict[OperatorBase], expectation: ExpectationBase, threshold: float = 1e-12, ) -> ListOrDict[Tuple[complex, complex]]: # Create new CircuitSampler to avoid breaking existing one's caches. sampler = CircuitSampler(self.quantum_instance) if isinstance(aux_operators, dict): list_op = ListOp(list(aux_operators.values())) else: list_op = ListOp(aux_operators) aux_op_meas = expectation.convert(StateFn(list_op, is_measurement=True)) aux_op_expect = aux_op_meas.compose( CircuitStateFn(self.ansatz.bind_parameters(parameters))) aux_op_expect_sampled = sampler.convert(aux_op_expect) # compute means values = np.real(aux_op_expect_sampled.eval()) # compute standard deviations variances = np.real( expectation.compute_variance(aux_op_expect_sampled)) if not isinstance(variances, np.ndarray) and variances == 0.0: # when `variances` is a single value equal to 0., our expectation value is exact and we # manually ensure the variances to be a list of the correct length variances = np.zeros(len(aux_operators), dtype=float) std_devs = np.sqrt(variances / self.quantum_instance.run_config.shots) # Discard values below threshold aux_op_means = values * (np.abs(values) > threshold) # zip means and standard deviations into tuples aux_op_results = zip(aux_op_means, std_devs) # Return None eigenvalues for None operators if aux_operators is a list. # None operators are already dropped in compute_minimum_eigenvalue if aux_operators is a dict. if isinstance(aux_operators, list): aux_operator_eigenvalues = [None] * len(aux_operators) key_value_iterator = enumerate(aux_op_results) else: aux_operator_eigenvalues = {} key_value_iterator = zip(aux_operators.keys(), aux_op_results) for key, value in key_value_iterator: if aux_operators[key] is not None: aux_operator_eigenvalues[key] = value return aux_operator_eigenvalues