Source code for qiskit_algorithms.time_evolvers.variational.solvers.ode.forward_euler_solver

# This code is part of a Qiskit project.
# (C) Copyright IBM 2023.
# This code is licensed under the Apache License, Version 2.0. You may
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"""Forward Euler ODE solver."""
from import Callable, Sequence

import numpy as np
from scipy.integrate import OdeSolver
from scipy.integrate._ivp.base import ConstantDenseOutput

[docs]class ForwardEulerSolver(OdeSolver): """Forward Euler ODE solver.""" def __init__( self, function: Callable, t0: float, y0: Sequence, t_bound: float, vectorized: bool = False, support_complex: bool = False, num_t_steps: int = 15, ): """ Forward Euler ODE solver that implements an interface from SciPy. Args: function: Right-hand side of the system. The calling signature is ``fun(t, y)``. Here ``t`` is a scalar, and there are two options for the ndarray ``y``: It can either have shape (n,); then ``fun`` must return array_like with shape (n,). Alternatively it can have shape (n, k); then ``fun`` must return an array_like with shape (n, k), i.e., each column corresponds to a single column in ``y``. The choice between the two options is determined by `vectorized` argument (see below). The vectorized implementation allows a faster approximation of the Jacobian by finite differences (required for this solver). t0: Initial time. y0: Initial state. t_bound: Boundary time - the integration won't continue beyond it. It also determines the direction of the integration. vectorized: Whether ``fun`` is implemented in a vectorized fashion. Default is False. support_complex: Whether integration in a complex domain should be supported. Generally determined by a derived solver class capabilities. Default is False. num_t_steps: Number of time steps for the forward Euler method. """ self._y_old = None self._step_length = (t_bound - t0) / num_t_steps super().__init__(function, t0, y0, t_bound, vectorized, support_complex) def _step_impl(self): """ Takes an Euler step. """ try: self._y_old = self.y self.y = list(np.add(self.y, self._step_length *, self.y))) self.t += self._step_length return True, None except Exception as ex: # pylint: disable=broad-except return False, f"Unknown ODE solver error: {str(ex)}." def _dense_output_impl(self): return ConstantDenseOutput(self.t_old, self.t, self._y_old)