def one_step_grid_search(self, current_step):
"""
Grid search on n-th pair of QAOA angles, where n=current_step.
Args:
current_step (int): specify on which layer do we perform search.
Returns:
best_beta, best_gamma (floats): best values of the beta and gamma found.
"""
self.qaoa_inst.steps = current_step
best_beta = None
best_gamma = None
best_energy = np.inf
fixed_betas = self.qaoa_inst.betas
fixed_gammas = self.qaoa_inst.gammas
beta_range = np.linspace(0, np.pi, self.grid_size)
gamma_range = np.linspace(0, 2 * np.pi, self.grid_size)
vqe = VQE(self.qaoa_inst.minimizer,
minimizer_args=self.qaoa_inst.minimizer_args,
minimizer_kwargs=self.qaoa_inst.minimizer_kwargs)
cost_hamiltonian = reduce(lambda x, y: x + y, self.qaoa_inst.cost_ham)
for beta in beta_range:
for gamma in gamma_range:
betas = np.append(fixed_betas, beta)
gammas = np.append(fixed_gammas, gamma)
stacked_params = np.hstack((betas, gammas))
program = self.qaoa_inst.get_parameterized_program()
energy = vqe.expectation(program(stacked_params),
cost_hamiltonian, self.samples,
self.qaoa_inst.qc)
print(beta, gamma, end="\r")
if energy < best_energy:
best_energy = energy
best_beta = beta
best_gamma = gamma
return best_beta, best_gamma
def simple_grid_search_angles(self, save_data=False):
"""
Finds optimal angles for QAOA by performing grid search on all the angles.
This is not recommended for higher values of steps parameter,
since it results in grid_size**(2*steps) evaluations.
Returns:
best_betas, best_gammas (np.arrays): best values of the betas and gammas found.
"""
best_betas = None
best_gammas = None
best_energy = np.inf
# For some reasons np.meshgrid returns columns in order, where values in second
# grow slower than in the first one. This a fix to it.
if self.qaoa_inst.steps == 1:
column_order = [0]
else:
column_order = [1, 0] + list(range(2, self.qaoa_inst.steps))
new_indices = np.argsort(column_order)
beta_ranges = [np.linspace(0, np.pi, self.grid_size)
] * self.qaoa_inst.steps
all_betas = np.vstack(np.meshgrid(*beta_ranges)).reshape(
self.qaoa_inst.steps, -1).T
all_betas = all_betas[:, column_order]
gamma_ranges = [np.linspace(0, 2 * np.pi, self.grid_size)
] * self.qaoa_inst.steps
all_gammas = np.vstack(np.meshgrid(*gamma_ranges)).reshape(
self.qaoa_inst.steps, -1).T
all_gammas = all_gammas[:, column_order]
vqe = VQE(self.qaoa_inst.minimizer,
minimizer_args=self.qaoa_inst.minimizer_args,
minimizer_kwargs=self.qaoa_inst.minimizer_kwargs)
cost_hamiltonian = reduce(lambda x, y: x + y, self.qaoa_inst.cost_ham)
all_energies = []
data_to_save = []
if save_data:
file_name = "_".join(
[str(self.m), "grid",
str(self.grid_size),
str(time.time())]) + ".csv"
for betas in all_betas:
for gammas in all_gammas:
stacked_params = np.hstack((betas, gammas))
program = self.qaoa_inst.get_parameterized_program()
energy = vqe.expectation(program(stacked_params),
cost_hamiltonian, self.samples,
self.qaoa_inst.qc)
all_energies.append(energy)
if self.verbose:
print(betas, gammas, energy, end="\r")
if save_data:
data_to_save.append(np.hstack([betas, gammas, energy]))
if energy < best_energy:
best_energy = energy
best_betas = betas
best_gammas = gammas
if self.verbose:
print("Lowest energy:", best_energy)
print("Angles:", best_betas, best_gammas)
if save_data:
np.savetxt(file_name, np.array(data_to_save), delimiter=",")
if self.visualize:
if self.qaoa_inst.steps == 1:
self.ax = plot_energy_landscape(all_betas,
all_gammas,
np.array(all_energies),
log_legend=True)
else:
plot_variance_landscape(all_betas, all_gammas,
np.array(all_energies))
return best_betas, best_gammas
