# Compute perfect collection of DDOT circuits, meaning that we want circuits that implement every
# computational basis state on each k-qubit subset at least once.
base_OT.compute_perfect_collection(method_name=method_name,
method_kwargs=method_kwargs)
# get list of circuits
DDOT_circuits = base_OT.circuits_list
# Calculate various properties of circuits (and print them)
base_OT.calculate_properties_of_circuits()
circuits_properties = base_OT.circuits_properties_dictionary
anf.cool_print("Properties of the family:\n", circuits_properties)
# print(circuits_properties)
if saving and anf.query_yes_no('Do you still wish to save?'):
dictionary_to_save = {'circuits_list': DDOT_circuits,
'circuits_properties': circuits_properties}
directory = anf.get_module_directory() + '/saved_data/data_circuits_collections/DDOT/' + \
'locality_%s' % subsets_locality + '/number_of_qubits_%s' % number_of_qubits + '/'
file_name = 'circuits_amount%s' % len(DDOT_circuits) + '_' + anf.gate_proper_date_string()
directory_to_save = anf.save_results_pickle(dictionary_to_save=dictionary_to_save,
directory=directory,
custom_name=file_name
)
'clusters_labels_list'], dictionary_data['neighborhoods']
# Get instance of correction data generator - it will generate correction matrices for marginals_dictionary
# based on provided noise model
correction_data_generator = CorrectionDataGenerator(
results_dictionary_ddt=dictionary_results,
bitstrings_right_to_left=bitstrings_right_to_left,
number_of_qubits=number_of_qubits,
marginals_dictionary=marginal_dictionaries_initial,
clusters_list=clusters_list,
neighborhoods=neighborhoods,
noise_matrices_dictionary=noise_matrices_dictionary_initial)
all_pairs = [[qi, qj] for qi in list_of_qubits for qj in list_of_qubits
if qj > qi]
# Get data needed to make corrections for two-qubit marginals_dictionary (for 2-local Hamiltonian problems)
correction_data = correction_data_generator.get_pairs_correction_data(
all_pairs, show_progress_bar=True)
if saving:
# Update dictionary to be saved
dictionary_data['correction_data'] = correction_data
# Save results
directory = tests_directory + 'mitigation_on_marginals/' + backend_name \
+ '/number_of_qubits_%s' % number_of_qubits + '/' + date + '/DDOT/'
anf.save_results_pickle(dictionary_data, directory,
'04_test_results_correction_data')
+ backend_name + "/number_of_qubits_%s" % number_of_qubits + "/counts_raw/" \
+ date_string + "/"
file_name = 'circuits_amount%s' % circuits_amount + '_' + date_string
dictionary_to_save = {
'true_qubits': dictionary_data['true_qubits'],
'experiment_name': "DDOT",
'circuits_labels': dictionary_data['circuits_labels'],
'SDK_name': backend_name,
'date': date_string,
'unprocessed_counts': unprocessed_results
}
anf.save_results_pickle(dictionary_to_save=dictionary_to_save,
directory=save_directory_raw_counts,
custom_name=file_name)
processed_results = fdt.convert_counts_overlapping_tomography(
counts_dictionary=unprocessed_results, experiment_name="DDOT")
save_directory_counts_processed = anf.get_module_directory() + "/saved_data/tomography_results/DDOT/" \
+ backend_name + "/number_of_qubits_%s" % number_of_qubits \
+ "/counts_processed/" + date_string + "/"
dictionary_to_save['results_dictionary_preprocessed'] = processed_results
anf.save_results_pickle(dictionary_to_save=dictionary_to_save,
directory=save_directory_counts_processed,
custom_name=file_name)
def run_batches(
batches,
backend_name,
shots=8192,
saving_IDs_dictionary={
'saving': False,
'directory': None,
'file_name': None,
'dictionary_to_save': {}
}):
# IBMQ.load_account()
# IBMQ.load_account()
# raise KeyError
saving = saving_IDs_dictionary['saving']
anf.cool_print('\nSending jobs_list to execution on: ', backend_name + '.')
anf.cool_print('Number of shots: ', str(shots) + ' .')
anf.cool_print('Target number of jobs_list: ', str(len(batches)) + ' .')
iterations_done = 0
wait_time_in_minutes = 10
print()
jobs = []
while iterations_done < len(batches):
anf.cool_print('job number:', str(iterations_done))
circuits = batches[iterations_done]
if backend_name in [
'qasm_simulator', 'statevector_simulator', 'unitary_simulator'
]:
backend = Aer.get_backend(backend_name)
else:
IBMQ.load_account()
provider = IBMQ.get_provider(group='open')
backend = provider.get_backend(backend_name)
try:
time.sleep(2)
qobj_id = 'first_circuit-' + circuits[
0].name + '-last_circuit-' + circuits[
-1].name + '-date-' + anf.get_date_string('-')
anf.cool_print("Sending quantum program to: ", backend_name + '.')
job = qiskit.execute(circuits,
backend,
shots=shots,
max_credits=200,
qobj_id=qobj_id)
if saving and backend_name not in [
'qasm_simulator', 'statevector_simulator',
'unitary_simulator'
]:
job_ID = job.job_id()
dict_to_save = saving_IDs_dictionary['dictionary_to_save']
dict_to_save['job_ID'] = job_ID
anf.save_results_pickle(
dictionary_to_save=dict_to_save,
directory=saving_IDs_dictionary['directory'],
custom_name=saving_IDs_dictionary['file_name'] +
'_job%s' % iterations_done)
jobs.append(job)
while job.status() == JobStatus.INITIALIZING:
print(job.status())
time.sleep(2)
anf.cool_print("Program sent for execution to: ",
backend_name + '.')
except BaseException as ex:
print('There was an error in the circuit!. Error = {}'.format(ex))
print(f'Waiting {wait_time_in_minutes} minute(s) before next try.')
time.sleep(wait_time_in_minutes * 60)
continue
print()
iterations_done += 1
return jobs