Open jmc595 opened 1 month ago
The key classes and functions of the GRAPE algorithm are described in Issue #1.
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)
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
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.
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.