def execute_example(qi,
unitary,
desired_bit_accuracy,
minimum_chance_of_success,
shots=512,
topology=None,
create_errors=False,
mu=0.5,
sigma=0.5):
nancillas, p_succes = error_estimate(desired_bit_accuracy,
minimum_chance_of_success)
qubits, extra_empty_bits = find_qubits_from_unitary(unitary,
nancillas,
topology=topology)
final_qasm = generate_qasm_code(nancillas,
qubits,
unitary,
extra_empty_bits=extra_empty_bits)
final_qasm = optimize(final_qasm, nancillas, qubits, extra_empty_bits)
if topology is not None:
final_qasm = map_to_topology(topology, final_qasm)
final_qasm = optimize(final_qasm, nancillas, qubits, extra_empty_bits)
if create_errors:
final_qasm = introduce_error(final_qasm, mu, sigma)
final_qasm = optimize(final_qasm, nancillas, qubits, extra_empty_bits)
backend_type = qi.get_backend_type_by_name('QX single-node simulator')
return qi.execute_qasm(final_qasm,
backend_type=backend_type,
number_of_shots=shots)
print(phase)
unitary_qasm = f"QASM\nRz q[0], {-phase*2*np.pi}"
unitary_matrix = np.array([[np.exp(phase * 1j * np.pi), 0],
[0, np.exp(-phase * 1j * np.pi)]])
unitary = unitary_matrix
custom_prepare = "prep_z q[0]\n X q[0]"
desired_bit_accuracy = 5
minimum_chance_of_success = 0.5
mu = 0.5
sigma = 0.5
error_toggle = 0
topology = None
shots = 512
# process
nancillas, p_succes = error_estimate(desired_bit_accuracy,
minimum_chance_of_success)
qubits, extra_empty_bits = find_qubits_from_unitary(unitary,
nancillas,
topology=topology)
final_qasm = generate_qasm_code(nancillas,
qubits,
unitary,
extra_empty_bits=extra_empty_bits,
custom_prepare=custom_prepare)
final_qasm = optimize(final_qasm, nancillas, qubits, extra_empty_bits)
if topology is not None:
final_qasm = map_to_topology(topology, final_qasm)
val = points[i]
#unitary = f"QASM\n" \
# f"Rz q[0], {-val}"
unitary = np.array([[np.exp(val * 0.5j), 0],
[0, np.exp(-val * 0.5j)]])
use_qi = qis[0]
if use_multiple:
qi_num = i % len(qis)
use_qi = qis[qi_num]
arguments.append([
use_qi, unitary, desired_bit_accuracy,
minimum_chance_of_success, 512, topology, use_error_model, mu,
sigma
])
start = time.time()
data = generate_data(arguments, True, file)
total = time.time() - start
print('Total time: ', total, ' seconds')
nancilla, p_succes = error_estimate(desired_bit_accuracy, 0.5)
data = to_array(data, desired_bit_accuracy, nancilla)
binary_fractions = decimal_to_binary_fracion(find_max_value_keys(data),
nancilla)
heatmap(data[1], name)
graph(binary_fractions)