default="png")
# Parse command line arguments
arguments = parser.parse_args()
# Format inputs for use in this script
directory_to_analyse = arguments.results_directory
log_file = arguments.log_file
all_bayes_csv = arguments.bayes_csv
qhl_mode = bool(arguments.qhl_mode)
further_qhl_mode = bool(arguments.further_qhl_mode)
run_info_file = arguments.run_info_file
system_measurements_file = arguments.system_measurements_file
exploration_rule = arguments.exploration_rules
gather_summary_results = bool(arguments.gather_summary_results)
true_exploration_class = qmla.get_exploration_class(
exploration_rules=exploration_rule, log_file=log_file)
latex_mapping_file = arguments.latex_mapping_file
figure_format = arguments.figure_format
probes_plot_file = arguments.probes_plot_file
results_collection_file = "{}/collect_analyses.p".format(directory_to_analyse)
if run_info_file is not None:
true_params_info = pickle.load(open(run_info_file, 'rb'))
true_params_dict = true_params_info['params_dict']
true_model = true_params_info['true_model']
else:
true_params_dict = None
true_model = true_exploration_class.true_model
true_model_latex = true_exploration_class.latex_name(true_model)
if not directory_to_analyse.endswith('/'):
directory_to_analyse += '/'
run_directory = arguments.run_directory
# Generate ES instances
exploration_class_attributes = {
'true_params_path': arguments.run_info_file,
'plot_probes_path': probes_plot_file,
'log_file': log_file
}
all_exploration_strategies = [exploration_rules]
alternative_exploration_strategies = arguments.alternative_exploration_strategies
all_exploration_strategies.extend(alternative_exploration_strategies)
all_exploration_strategies = list(set(all_exploration_strategies))
unique_exploration_classes = {
gr: qmla.get_exploration_class(exploration_rules=gr,
**exploration_class_attributes)
for gr in all_exploration_strategies
}
exploration_class = unique_exploration_classes[exploration_rules]
probe_max_num_qubits_all_exploration_strategies = max(
[gr.max_num_probe_qubits for gr in unique_exploration_classes.values()])
# Use setup so far to generate parameters
true_model = exploration_class.true_model
qmla.set_shared_parameters(
exploration_class=exploration_class,
run_info_file=arguments.run_info_file,
all_exploration_strategies=all_exploration_strategies,
run_directory=run_directory,
def time_required(
exploration_rule, # ie true growth generator for QHL
exploration_strategies,
num_particles,
num_experiments,
num_processes=1,
resource_reallocation=False,
# num_bayes_times=None,
minimum_allowed_time=100,
insurance_factor=2.5,
**kwargs):
times_reqd = {}
num_hamiltonians_per_model = (num_particles * (2 * num_experiments))
parallelisability = {}
print("exploration strategies:", exploration_strategies)
# print("num models by shape:", generator_max_num_models_by_shape)
total_time_required = 0
for gen in exploration_strategies:
try:
exploration_class = qmla.get_exploration_class(
exploration_rules=gen)
generator_max_num_models_by_shape = exploration_class.max_num_models_by_shape
print("Got time required from growth generator {}".format(gen))
print("Num models by num qubits:",
generator_max_num_models_by_shape)
except BaseException:
generator_max_num_models_by_shape = max_num_models_by_shape[gen]
parallelisability[
gen] = exploration_class.num_processes_to_parallelise_over
max_num_qubits = exploration_class.max_num_qubits
for q in range(1, max_num_qubits + 1):
time_per_hamiltonian = hamiltonian_exponentiation_times[q]
try:
num_models_this_dimension = generator_max_num_models_by_shape[
q]
except BaseException:
print(
"num models {} not in generator_max_num_models_by_shape; taking default"
.format(q))
num_models_this_dimension = generator_max_num_models_by_shape[
'other']
print("ES:", gen, "max num models for ", q, "qubits:",
num_models_this_dimension)
time_this_dimension = (num_hamiltonians_per_model *
time_per_hamiltonian *
num_models_this_dimension)
total_time_required += time_this_dimension
total_time_required = (insurance_factor * np.round(total_time_required))
times_reqd['qmd'] = max(minimum_allowed_time, int(total_time_required))
# Get time for QHL
try:
true_model = exploration_class.true_model
except BaseException:
raise
highest_parallelisability = max(parallelisability.values())
times_reqd['num_processes'] = highest_parallelisability
true_dimension = qmla.get_num_qubits(true_model)
qhl_insurance_factor = 1.5 # buffer to ensure finished in time
qhl_time = (qhl_insurance_factor *
hamiltonian_exponentiation_times[true_dimension] *
num_particles * num_experiments)
times_reqd['qhl'] = max(minimum_allowed_time, int(qhl_time))
# For further qhl, want to account for possibility
# that winning model is of maximum allowed dimension,
# so need to request enough time for that case.
further_qhl_time = (hamiltonian_exponentiation_times[max_num_qubits] *
num_hamiltonians_per_model)
times_reqd['fqhl'] = max(minimum_allowed_time, int(further_qhl_time))
for k in ['qmd', 'qhl', 'fqhl']:
times_reqd[k] *= exploration_class.timing_insurance_factor
times_reqd[k] = max(minimum_allowed_time, int(times_reqd[k]))
times_reqd[k] = int(times_reqd[k])
print("Time to request:\n", times_reqd)
return times_reqd
type=str,
default='Unknown')
parser.add_argument('-exp',
'--experimental_data',
help='Use experimental data if provided',
type=int,
default=0)
arguments = parser.parse_args()
results_directory = arguments.results_directory
output_file_name = arguments.output_file_name
exploration_rules = arguments.exploration_rules
exploration_class = qmla.get_exploration_class(
exploration_rules=exploration_rules,
use_experimental_data=arguments.experimental_data,
log_file=arguments.log_file)
variables = vars(arguments)
# and some others arguments not explicitly set in launch script
# variables['measurement_type'] = exploration_class.measurement_type
variables[
'expectation_value_func'] = exploration_class.expectation_value_subroutine.__name__
variables['heuristic'] = exploration_class.model_heuristic_subroutine.__name__
variables[
'probe_generation_function'] = exploration_class.plot_probes_generation_subroutine.__name__
variables[
'plot_probes_generation_subroutine'] = exploration_class.plot_probes_generation_subroutine.__name__
qmla.analysis.combine_analysis_plots(results_directory=results_directory,
# Parse command line arguments
arguments = parser.parse_args()
# Format inputs for use in this script
directory_to_analyse = arguments.results_directory
log_file = arguments.log_file
all_bayes_csv = arguments.bayes_csv
qhl_mode = bool(arguments.qhl_mode)
further_qhl_mode = bool(arguments.further_qhl_mode)
run_info_file = arguments.run_info_file
system_measurements_file = arguments.system_measurements_file
exploration_rule = arguments.exploration_rules
gather_summary_results = bool(arguments.gather_summary_results)
true_exploration_class = qmla.get_exploration_class(
exploration_rules=exploration_rule,
true_params_path=arguments.run_info_file,
plot_probes_path=arguments.probes_plot_file,
log_file=log_file)
true_exploration_class.get_true_parameters()
latex_mapping_file = arguments.latex_mapping_file
figure_format = arguments.figure_format
probes_plot_file = arguments.probes_plot_file
results_collection_file = "{}/collect_analyses.p".format(directory_to_analyse)
if run_info_file is not None:
true_params_info = pickle.load(open(run_info_file, 'rb'))
true_params_dict = true_params_info['params_dict']
true_model = true_params_info['true_model']
else:
true_params_dict = None
true_model = true_exploration_class.true_model