GRAPE Dummy Function

[jmc595]

The goal is to replace GRAPE with GRAFS. This issue creates a dummy function which could be dropped in place of GRAPE. This function takes the same input types and returns and output of the same type but with dummy values.

[jmc595]

The key classes and functions of the GRAPE algorithm are described in Issue #1.

[jmc595]

The dummy function for GRAPE takes the same input and returns an output of the correct type and shape:

import numpy as np

def dummy_grape(H, initial_pulse, target_state, num_iterations): 

    """
    A dummy function to replace the GRAPE algorithm.

    Parameters:
    - H: numpy.ndarray
        The Hamiltonian matrix.
    - initial_pulse: numpy.ndarray
        The initial guess for the control pulses.
    - target_state: numpy.ndarray
        The target quantum state to achieve.
    - num_iterations: int
        The number of iterations for the optimization.

    Returns:
    - optimized_pulse: numpy.ndarray
        The optimized control pulses (dummy values).
    - final_state: numpy.ndarray
        The final quantum state (dummy values).
    - fidelity: float
        The fidelity of the final state compared to the target state (dummy value).
    """

    pulse_shape = initial_pulse.shape
    state_shape = target_state.shape

    # Create dummy outputs
    optimized_pulse = np.zeros(pulse_shape)
    final_state = np.zeros(state_shape)
    fidelity = 0.0

    return optimized_pulse, final_state, fidelity

# Examples
H = np.array([[0, 1], [1, 0]])  # Example Hamiltonian
initial_pulse = np.array([0.1, 0.2, 0.3])  # Example initial pulse
target_state = np.array([0, 1])  # Example target state
num_iterations = 100

optimized_pulse, final_state, fidelity = dummy_grape(H, initial_pulse, target_state, num_iterations)
print("Optimized Pulse:", optimized_pulse)
print("Final State:", final_state)
print("Fidelity:", fidelity)

[jmc595]

In the code:

H : is the Hamiltonian matrix

initial_pulse: is the initial guess for the control pulses

target_state: is the target quantum state

num_iterations: is the number of iterations for optimization

[jmc595]

The dummy_grape function returns optimized_pulse, final_state, and fidelity, all filled with zeros or dummy values. This function can be used in place of the actual GRAPE algorithm for testing purposes.