FAQ¤
How can I incorporate drive constraints (as opposed to costs)?¤
Constraints can be incorporated at the level of the Hamiltonian function:
a = dq.destroy(5)
H0 = dq.dag(a) @ dq.dag(a) @ a @ a
H1s = [a + dq.dag(a), 1j * (a - dq.dag(a))]
max_amp = 2.0 * jnp.pi * 0.01
tsave = jnp.linspace(0, 40.0, 31)
parameters = jnp.zeros((len(H1s), len(tsave) - 1))
def H_pwc(drive_params):
H = H0
for idx, _H1 in enumerate(H1s):
clipped_drive = jnp.clip(
drive_params[idx],
a_min=-max_amp,
a_max=max_amp,
)
H += dq.pwc(tsave, clipped_drive, _H1)
return H
How do I initialize random initial pulses?¤
This can be done by utilizing existing rng functionality from jax. Lets create a random initial guess for the pwc control above with values between +1 and -1.
key = jax.random.PRNGKey(31)
parameters = 2.0 * jax.random.uniform(key, (len(H1s), len(tsave) - 1)) - 1.0
How do I access the saved information?¤
If a filepath is passed to optimize, the parameters from each epoch are saved along with the individual values of each cost function and the total cost. This data can be extracted via (assuming the data has been saved in the file 'tmp.h5py')
data_dict, param_dict = ql.extract_info_from_h5('tmp.h5py')
data_dict contains the optimized parameters as well as cost function info, and param_dict contains the options passed to optimize.
You haven't defined my favorite cost function?!¤
No worries! See the API of ql.custom_cost() as well as ql.custom_control_cost() for examples of how to define custom cost functions.