def test_tasks() -> None:
"""Task creation is tested separately in the absence of Von Neumann eqn"""
model = Model()
model.read_config("test/test_model_spam.cfg")
pmap = ParameterMap(model=model, generator=gen)
pmap.read_config("test/instructions.cfg")
assert list(model.tasks.keys()) == ["init_ground", "conf_matrix", "meas_rescale"]
def test_calibration_cmaes() -> None:
"""Create a C2 style Optimizer object and run calibration
with a mock_ORBIT function. Check if the goal in the optimizer
correctly reflects the constant goal returned by the mock_ORBIT.
"""
with open(OPT_CONFIG_FILE_NAME, "r") as cfg_file:
cfg = hjson.load(cfg_file)
pmap = ParameterMap()
pmap.read_config(cfg.pop("instructions"))
pmap.set_opt_map(
[[tuple(par) for par in pset] for pset in cfg.pop("gateset_opt_map")]
)
exp = Experiment(pmap=pmap)
algo_options = cfg.pop("options")
run_name = cfg.pop("run_name")
opt = Calibration(
dir_path=LOGDIR,
run_name=run_name,
eval_func=mock_ORBIT,
pmap=pmap,
exp_right=exp,
algorithm=algorithms.cmaes,
options=algo_options,
)
opt.run()
assert opt.current_best_goal == RESULT_VAL
import pytest
from c3.c3objs import Quantity
from c3.libraries.envelopes import envelopes
from c3.signal.gates import Instruction
from c3.signal.pulse import Envelope, Carrier
from c3.model import Model
from c3.generator.generator import Generator
from c3.parametermap import ParameterMap
from c3.experiment import Experiment as Exp
model = Model()
model.read_config("test/test_model.cfg")
gen = Generator()
gen.read_config("test/generator.cfg")
pmap = ParameterMap(model=model, generator=gen)
pmap.read_config("test/instructions.cfg")
@pytest.mark.unit
def test_name_collision() -> None:
broken_model = Model()
with pytest.raises(KeyError):
broken_model.read_config("test/test_model_breaking.cfg")
@pytest.mark.unit
def test_subsystems() -> None:
assert list(model.subsystems.keys()) == ["Q1", "Q2", "Q3", "Q4", "Q5", "Q6"]
@pytest.mark.unit
def run_cfg(cfg, opt_config_filename, debug=False):
"""Execute an optimization problem described in the cfg file.
Parameters
----------
cfg : Dict[str, Union[str, int, float]]
Configuration file containing optimization options and information needed to completely
setup the system and optimization problem.
debug : bool, optional
Skip running the actual optimization, by default False
"""
optim_type = cfg.pop("optim_type")
optim_lib = {
"C1": OptimalControl,
"C2": Calibration,
"C3": ModelLearning,
"C3_confirm": ModelLearning,
"confirm": ModelLearning,
"SET": Sensitivity,
}
if not optim_type in optim_lib:
raise Exception("C3:ERROR:Unknown optimization type specified.")
tf_utils.tf_setup()
with tf.device("/CPU:0"):
model = None
gen = None
exp = None
prop_meth = cfg.pop("propagation_method", None)
if "model" in cfg:
model = Model()
model.read_config(cfg.pop("model"))
if "generator" in cfg:
gen = Generator()
gen.read_config(cfg.pop("generator"))
if "instructions" in cfg:
pmap = ParameterMap(model=model, generator=gen)
pmap.read_config(cfg.pop("instructions"))
exp = Experiment(pmap, prop_method=prop_meth)
if "exp_cfg" in cfg:
exp = Experiment(prop_method=prop_meth)
exp.read_config(cfg.pop("exp_cfg"))
if exp is None:
print(
"C3:STATUS: No instructions specified. Performing quick setup."
)
exp = Experiment(prop_method=prop_meth)
exp.quick_setup(cfg)
exp.set_opt_gates(cfg.pop("opt_gates", None))
if "gateset_opt_map" in cfg:
exp.pmap.set_opt_map([[tuple(par) for par in pset]
for pset in cfg.pop("gateset_opt_map")])
if "exp_opt_map" in cfg:
exp.pmap.set_opt_map([[tuple(par) for par in pset]
for pset in cfg.pop("exp_opt_map")])
opt = optim_lib[optim_type](**cfg, pmap=exp.pmap)
opt.set_exp(exp)
opt.set_created_by(opt_config_filename)
if "initial_point" in cfg:
initial_points = cfg["initial_point"]
if isinstance(initial_points, str):
initial_points = [initial_points]
elif isinstance(initial_points, list):
pass
else:
raise Warning(
"initial_point has to be a path or a list of paths.")
for init_point in initial_points:
try:
opt.load_best(init_point)
print("C3:STATUS:Loading initial point from : "
f"{os.path.abspath(init_point)}")
except FileNotFoundError as fnfe:
raise Exception(
f"C3:ERROR:No initial point found at "
f"{os.path.abspath(init_point)}. ") from fnfe
if optim_type == "C1":
if "adjust_exp" in cfg:
try:
adjust_exp = cfg["adjust_exp"]
opt.load_model_parameters(adjust_exp)
print("C3:STATUS:Loading experimental values from : "
f"{os.path.abspath(adjust_exp)}")
except FileNotFoundError as fnfe:
raise Exception(
f"C3:ERROR:No experimental values found at "
f"{os.path.abspath(adjust_exp)} "
"Continuing with default.") from fnfe
if not debug:
opt.run()
optim_type = cfg["optim_type"]
tf_utils.tf_setup()
with tf.device("/CPU:0"):
model = None
gen = None
if "model" in cfg:
model = Model()
model.read_config(cfg["model"])
if "generator" in cfg:
gen = Generator()
gen.read_config(cfg["generator"])
if "instructions" in cfg:
pmap = ParameterMap(model=model, generator=gen)
pmap.read_config(cfg["instructions"])
exp = Experiment(pmap)
else:
print(
"C3:STATUS: No instructions specified. Performing quick setup."
)
exp = Experiment()
exp.quick_setup(opt_config)
if optim_type == "C1":
opt = parsers.create_c1_opt(opt_config, exp)
if cfg["include_model"]:
opt.include_model()
elif optim_type == "C2":
eval_func = cfg["eval_func"]
opt = parsers.create_c2_opt(opt_config, eval_func)