# Source code for qiskit.algorithms.time_evolvers.time_evolution_result

```
# This code is part of Qiskit.
#
# (C) Copyright IBM 2021, 2023.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Class for holding time evolution result."""
from __future__ import annotations
import numpy as np
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector
from qiskit.algorithms.list_or_dict import ListOrDict
from ..algorithm_result import AlgorithmResult
[docs]class TimeEvolutionResult(AlgorithmResult):
"""
Class for holding time evolution result.
Attributes:
evolved_state (QuantumCircuit|Statevector): An evolved quantum state.
aux_ops_evaluated (ListOrDict[tuple[complex, complex]] | None): Optional list of
observables for which expected values on an evolved state are calculated. These values
are in fact tuples formatted as (mean, standard deviation).
observables (ListOrDict[tuple[np.ndarray, np.ndarray]] | None): Optional list of
observables for which expected on an evolved state are calculated at each timestep.
These values are in fact lists of tuples formatted as (mean, standard deviation).
times (np.array | None): Optional list of times at which each observable has been evaluated.
"""
def __init__(
self,
evolved_state: QuantumCircuit | Statevector,
aux_ops_evaluated: ListOrDict[tuple[complex, complex]] | None = None,
observables: ListOrDict[tuple[np.ndarray, np.ndarray]] | None = None,
times: np.ndarray | None = None,
):
"""
Args:
evolved_state: An evolved quantum state.
aux_ops_evaluated: Optional list of observables for which expected values on an evolved
state are calculated. These values are in fact tuples formatted as (mean, standard
deviation).
observables: Optional list of observables for which expected values for each timestep.
These values are in fact tuples formatted as (mean array, standard deviation array).
times: Optional list of times at which each observable has been evaluated.
"""
self.evolved_state = evolved_state
self.aux_ops_evaluated = aux_ops_evaluated
self.observables = observables
self.times = times
```