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 from_dict(self, cfg: Dict) -> None:
"""
Load experiment from dictionary
"""
model = Model()
model.fromdict(cfg["model"])
generator = Generator()
generator.fromdict(cfg["generator"])
pmap = ParameterMap(model=model, generator=generator)
pmap.fromdict(cfg["instructions"])
if "options" in cfg:
for k, v in cfg["options"].items():
self.__dict__[k] = v
self.pmap = pmap
zero_ones[0] = 0
val1 = one_zeros * m00_q1 + zero_ones * m01_q1
val2 = one_zeros * m00_q2 + zero_ones * m01_q2
min = one_zeros * 0.8 + zero_ones * 0.0
max = one_zeros * 1.0 + zero_ones * 0.2
confusion_row1 = Quantity(value=val1, min_val=min, max_val=max, unit="")
confusion_row2 = Quantity(value=val2, min_val=min, max_val=max, unit="")
conf_matrix = ConfusionMatrix(Q1=confusion_row1, Q2=confusion_row2)
init_temp = 50e-3
init_ground = InitialiseGround(init_temp=Quantity(
value=init_temp, min_val=-0.001, max_val=0.22, unit="K"))
model = Model(
[S], # Individual, self-contained components
[drive], # Interactions between components
[conf_matrix, init_ground], # SPAM processing
)
model.set_dressed(False)
hdrift, hks = model.get_Hamiltonians()
@pytest.mark.unit
def test_SNAIL_eigenfrequencies_1() -> None:
"Eigenfrequency of SNAIL"
assert (hdrift[1, 1] - hdrift[0, 0] == freq_S * 2 * np.pi
) # for the 0.2dev version, comment out the 2 pi
def test_name_collision() -> None:
broken_model = Model()
with pytest.raises(KeyError):
broken_model.read_config("test/test_model_breaking.cfg")
import numpy as np
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"]
# t2star=Qty(value=t2star_tc, min_val=1e-6, max_val=90e-6, unit="s"),
# temp=Qty(value=init_temp, min_val=0.0, max_val=0.12, unit="K"),
)
q1q2 = Coupling(
name="Q1-Q2",
connected=["Qubit1", "Qubit2"],
strength=Qty(value=coupling_strength,
min_val=0 * 1e4,
max_val=200e6,
unit="Hz 2pi"),
hamiltonian_func=hamiltonians.int_XX,
)
model = Model(subsystems=[q1, q2],
couplings=[q1q2],
max_excitations=cut_excitations)
model.set_lindbladian(False)
model.set_dressed(True)
model.set_FR(True)
# ### MAKE GENERATOR
lo = devices.LO(name="lo", resolution=sim_res)
awg = devices.AWG(name="awg", resolution=awg_res)
dig_to_an = devices.DigitalToAnalog(name="dac", resolution=sim_res)
resp = devices.ResponseFFT(
name="resp",
rise_time=Qty(value=0.3e-9, min_val=0.05e-9, max_val=0.6e-9, unit="s"),
resolution=sim_res,
)
mixer = devices.Mixer(name="mixer")
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()
def quick_setup(self, cfg) -> None:
"""
Load a quick setup cfg and create all necessary components.
Parameters
----------
cfg : Dict
Configuration options
"""
model = Model()
model.read_config(cfg["model"])
gen = Generator()
gen.read_config(cfg["generator"])
single_gate_time = cfg["single_qubit_gate_time"]
v2hz = cfg["v2hz"]
instructions = []
sideband = cfg.pop("sideband", None)
for gate_name, props in cfg["single_qubit_gates"].items():
target_qubit = model.subsystems[props["qubits"]]
instr = Instruction(
name=props["name"],
targets=[model.names.index(props["qubits"])],
t_start=0.0,
t_end=single_gate_time,
channels=[target_qubit.drive_line],
)
instr.quick_setup(
target_qubit.drive_line,
target_qubit.params["freq"].get_value() / 2 / np.pi,
single_gate_time,
v2hz,
sideband,
)
instructions.append(instr)
for gate_name, props in cfg["two_qubit_gates"].items():
qubit_1 = model.subsystems[props["qubit_1"]]
qubit_2 = model.subsystems[props["qubit_2"]]
instr = Instruction(
name=gate_name,
targets=[
model.names.index(props["qubit_1"]),
model.names.index(props["qubit_2"]),
],
t_start=0.0,
t_end=props["gate_time"],
channels=[qubit_1.drive_line, qubit_2.drive_line],
)
instr.quick_setup(
qubit_1.drive_line,
qubit_1.params["freq"].get_value() / 2 / np.pi,
props["gate_time"],
v2hz,
sideband,
)
instr.quick_setup(
qubit_2.drive_line,
qubit_2.params["freq"].get_value() / 2 / np.pi,
props["gate_time"],
v2hz,
sideband,
)
instructions.append(instr)
self.pmap = ParameterMap(instructions, generator=gen, model=model)