if __name__ == "__main__":
# update hyperparameters with command line arguments
HP = parse_arguments(HP)
# set the target state
target_state = HP['target_state_fn'](cutoff=HP['cutoff'], **HP['state_params'])
HP['modes'] = int(np.log(target_state.shape[0])/np.log(HP['cutoff']))
print('------------------------------------------------------------------------')
print('Hyperparameters:')
print('------------------------------------------------------------------------')
for key, val in HP.items():
print("{}: {}".format(key, val))
print('------------------------------------------------------------------------')
# calculate the learnt state and return the gate parameters
print('Constructing variational quantum circuit...')
ket, parameters = variational_quantum_circuit(**HP)
# flatten ket to take into account two mode states
ket = tf.reshape(ket, [-1])
# perform the optimization
print('Beginning optimization...')
res = optimize(ket, target_state, parameters, **HP)
# save plots
print('Generating plots...')
save_plots(res['learnt_state'], target_state, res['cost_progress'], **HP)
)
for key, val in HP.items():
print("{}: {}".format(key, val))
print(
'------------------------------------------------------------------------'
)
# produce a batch_size array of one mode initial Fock states
in_ket = np.zeros([HP['gate_cutoff'], HP['cutoff']])
np.fill_diagonal(in_ket, 1)
if HP['modes'] == 2:
# take the outer product of the one mode input states
in_ket = np.einsum('ij,kl->ikjl', in_ket, in_ket)
# reshape to be the correct shape for Strawberry Fields
in_ket = in_ket.reshape(HP['gate_cutoff']**2, HP['cutoff'],
HP['cutoff'])
# calculate the learnt state and return the gate parameters
print('Constructing variational quantum circuit...')
ket, parameters = variational_quantum_circuit(input_state=in_ket, **HP)
# perform the optimization
print('Beginning optimization...')
res = optimize(ket, target_unitary, parameters, **HP)
# save plots
print('Generating plots...')
save_plots(target_unitary, res['learnt_unitary'], res['eq_state_learnt'],
res['eq_state_target'], res['cost_progress'], **HP)