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 pwc(model: Model, gen: Generator, instr: Instruction) -> Dict:
"""
Solve the equation of motion (Lindblad or Schrรถdinger) for a given control
signal and Hamiltonians.
Parameters
----------
signal: dict
Waveform of the control signal per drive line.
gate: str
Identifier for one of the gates.
Returns
-------
unitary
Matrix representation of the gate.
"""
signal = gen.generate_signals(instr)
# Why do I get 0.0 if I print gen.resolution here?! FR
ts = []
if model.controllability:
h0, hctrls = model.get_Hamiltonians()
signals = []
hks = []
for key in signal:
signals.append(signal[key]["values"])
ts = signal[key]["ts"]
hks.append(hctrls[key])
signals = tf.cast(signals, tf.complex128)
hks = tf.cast(hks, tf.complex128)
else:
h0 = model.get_Hamiltonian(signal)
ts_list = [sig["ts"][1:] for sig in signal.values()]
ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0))
hks = None
signals = None
if not np.all(
tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0])
):
raise Exception("C3Error:Something with the times happend.")
if not np.all(
tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0])
):
raise Exception("C3Error:Something with the times happend.")
dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128)
batch_size = tf.constant(len(h0), tf.int32)
dUs = tf_batch_propagate(h0, hks, signals, dt, batch_size=batch_size)
U = tf_matmul_left(tf.cast(dUs, tf.complex128))
if model.max_excitations:
U = model.blowup_excitations(tf_matmul_left(tf.cast(dUs, tf.complex128)))
dUs = tf.vectorized_map(model.blowup_excitations, dUs)
return {"U": U, "dUs": dUs, "ts": ts}
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
def _get_hs_of_t_ts_controlled(
model: Model, gen: Generator, instr: Instruction, prop_res=1
) -> Dict:
"""
Return a Dict containing:
- a list of
H(t) = H_0 + sum_k c_k H_k.
- time slices ts
- timestep dt
Parameters
----------
prop_res : tf.float
resolution required by the propagation method
h0 : tf.tensor
Drift Hamiltonian.
hks : list of tf.tensor
List of control Hamiltonians.
cflds_t : array of tf.float
Vector of control field values at time t.
ts : float
Length of one time slice.
"""
Hs = []
ts = []
gen.resolution = prop_res * gen.resolution
signal = gen.generate_signals(instr)
h0, hctrls = model.get_Hamiltonians()
signals = []
hks = []
for key in signal:
signals.append(signal[key]["values"])
ts = signal[key]["ts"]
hks.append(hctrls[key])
cflds = tf.cast(signals, tf.complex128)
hks = tf.cast(hks, tf.complex128)
for ii in range(cflds[0].shape[0]):
cf_t = []
for fields in cflds:
cf_t.append(tf.cast(fields[ii], tf.complex128))
Hs.append(sum_h0_hks(h0, hks, cf_t))
dt = tf.constant(ts[1 * prop_res].numpy() - ts[0].numpy(), dtype=tf.complex128)
return {"Hs": Hs, "ts": ts[::prop_res], "dt": dt}
def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None) -> Dict:
prop_res = 2
dim = model.tot_dim
Hs = []
ts = []
dUs = []
dict_vals = get_hs_of_t_ts(model, gen, instr, prop_res)
Hs = dict_vals["Hs"]
ts = dict_vals["ts"]
dt = dict_vals["dt"]
dUs = gen_dus_rk4(Hs, dt, dim)
U = gen_u_rk4(Hs, dt, dim)
if model.max_excitations:
U = model.blowup_excitations(U)
dUs = tf.vectorized_map(model.blowup_excitations, dUs)
return {"U": U, "dUs": dUs, "ts": ts}
def _get_hs_of_t_ts(
model: Model, gen: Generator, instr: Instruction, prop_res=1
) -> Dict:
"""
Return a Dict containing:
- a list of
H(t) = H_0 + sum_k c_k H_k.
- time slices ts
- timestep dt
Parameters
----------
prop_res : tf.float
resolution required by the propagation method
h0 : tf.tensor
Drift Hamiltonian.
hks : list of tf.tensor
List of control Hamiltonians.
cflds_t : array of tf.float
Vector of control field values at time t.
ts : float
Length of one time slice.
"""
Hs = []
ts = []
gen.resolution = prop_res * gen.resolution
signal = gen.generate_signals(instr)
Hs = model.get_Hamiltonian(signal)
ts_list = [sig["ts"][1:] for sig in signal.values()]
ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0))
if not np.all(tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0])):
raise Exception("C3Error:Something with the times happend.")
if not np.all(tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0])):
raise Exception("C3Error:Something with the times happend.")
dt = tf.constant(ts[1 * prop_res].numpy() - ts[0].numpy(), dtype=tf.complex128)
return {"Hs": Hs, "ts": ts[::prop_res], "dt": dt}
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"]
def create_experiment():
lindblad = False
dressed = True
qubit_lvls = 3
freq_q1 = 5e9 * 2 * np.pi
freq_q2 = 5.6e9 * 2 * np.pi
anhar_q1 = -210e6 * 2 * np.pi
anhar_q2 = -240e6 * 2 * np.pi
coupling_strength = 20e6 * 2 * np.pi
t1_q1 = 27e-6
t1_q2 = 23e-6
t2star_q1 = 39e-6
t2star_q2 = 31e-6
init_temp = 0
qubit_temp = 0
t_final = 7e-9 # Time for single qubit gates
sim_res = 100e9
awg_res = 2e9
sideband = 50e6 * 2 * np.pi
lo_freq_q1 = 5e9 * 2 * np.pi + sideband
lo_freq_q2 = 5.6e9 * 2 * np.pi + sideband
# ### MAKE MODEL
q1 = chip.Qubit(
name="Q1",
desc="Qubit 1",
freq=Qty(
value=freq_q1,
min_val=4.995e9 * 2 * np.pi,
max_val=5.005e9 * 2 * np.pi,
unit="Hz 2pi",
),
anhar=Qty(
value=anhar_q1,
min_val=-380e6 * 2 * np.pi,
max_val=-120e6 * 2 * np.pi,
unit="Hz 2pi",
),
hilbert_dim=qubit_lvls,
t1=Qty(value=t1_q1, min_val=1e-6, max_val=90e-6, unit="s"),
t2star=Qty(value=t2star_q1, min_val=10e-6, max_val=90e-6, unit="s"),
temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit="K"),
)
q2 = chip.Qubit(
name="Q2",
desc="Qubit 2",
freq=Qty(
value=freq_q2,
min_val=5.595e9 * 2 * np.pi,
max_val=5.605e9 * 2 * np.pi,
unit="Hz 2pi",
),
anhar=Qty(
value=anhar_q2,
min_val=-380e6 * 2 * np.pi,
max_val=-120e6 * 2 * np.pi,
unit="Hz 2pi",
),
hilbert_dim=qubit_lvls,
t1=Qty(value=t1_q2, min_val=1e-6, max_val=90e-6, unit="s"),
t2star=Qty(value=t2star_q2, min_val=10e-6, max_val=90e-6, unit="s"),
temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit="K"),
)
q1q2 = chip.Coupling(
name="Q1-Q2",
desc="coupling",
comment="Coupling qubit 1 to qubit 2",
connected=["Q1", "Q2"],
strength=Qty(
value=coupling_strength,
min_val=-1 * 1e3 * 2 * np.pi,
max_val=200e6 * 2 * np.pi,
unit="Hz 2pi",
),
hamiltonian_func=hamiltonians.int_XX,
)
drive = chip.Drive(
name="d1",
desc="Drive 1",
comment="Drive line 1 on qubit 1",
connected=["Q1"],
hamiltonian_func=hamiltonians.x_drive,
)
drive2 = chip.Drive(
name="d2",
desc="Drive 2",
comment="Drive line 2 on qubit 2",
connected=["Q2"],
hamiltonian_func=hamiltonians.x_drive,
)
phys_components = [q1, q2]
line_components = [drive, drive2, q1q2]
init_ground = tasks.InitialiseGround(
init_temp=Qty(value=init_temp, min_val=-0.001, max_val=0.22, unit="K"))
task_list = [init_ground]
model = Mdl(phys_components, line_components, task_list)
model.set_lindbladian(lindblad)
model.set_dressed(dressed)
# ### MAKE GENERATOR
generator = Gnr(
devices={
"LO":
devices.LO(name="lo", resolution=sim_res, outputs=1),
"AWG":
devices.AWG(name="awg", resolution=awg_res, outputs=1),
"DigitalToAnalog":
devices.DigitalToAnalog(name="dac",
resolution=sim_res,
inputs=1,
outputs=1),
"Response":
devices.Response(
name="resp",
rise_time=Qty(value=0.3e-9,
min_val=0.05e-9,
max_val=0.6e-9,
unit="s"),
resolution=sim_res,
inputs=1,
outputs=1,
),
"Mixer":
devices.Mixer(name="mixer", inputs=2, outputs=1),
"VoltsToHertz":
devices.VoltsToHertz(
name="v_to_hz",
V_to_Hz=Qty(value=1e9,
min_val=0.9e9,
max_val=1.1e9,
unit="Hz/V"),
inputs=1,
outputs=1,
),
},
chains={
"d1": {
"LO": [],
"AWG": [],
"DigitalToAnalog": ["AWG"],
"Response": ["DigitalToAnalog"],
"Mixer": ["LO", "Response"],
"VoltsToHertz": ["Mixer"],
},
"d2": {
"LO": [],
"AWG": [],
"DigitalToAnalog": ["AWG"],
"Response": ["DigitalToAnalog"],
"Mixer": ["LO", "Response"],
"VoltsToHertz": ["Mixer"],
},
},
)
generator.devices["awg"].enable_drag_2()
# ### MAKE GATESET
gateset = gates.GateSet()
gauss_params_single = {
"amp":
Qty(value=0.45, min_val=0.4, max_val=0.6, unit="V"),
"t_final":
Qty(value=t_final,
min_val=0.5 * t_final,
max_val=1.5 * t_final,
unit="s"),
"sigma":
Qty(value=t_final / 4,
min_val=t_final / 8,
max_val=t_final / 2,
unit="s"),
"xy_angle":
Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit="rad"),
"freq_offset":
Qty(
value=-sideband - 0.5e6 * 2 * np.pi,
min_val=-53 * 1e6 * 2 * np.pi,
max_val=-47 * 1e6 * 2 * np.pi,
unit="Hz 2pi",
),
"delta":
Qty(value=-1, min_val=-5, max_val=3, unit=""),
}
gauss_env_single = pulse.Envelope(
name="gauss",
desc="Gaussian comp for single-qubit gates",
params=gauss_params_single,
shape=envelopes.gaussian_nonorm,
)
nodrive_env = pulse.Envelope(
name="no_drive",
params={
"t_final":
Qty(value=t_final,
min_val=0.5 * t_final,
max_val=1.5 * t_final,
unit="s")
},
shape=envelopes.no_drive,
)
carrier_parameters = {
"freq":
Qty(
value=lo_freq_q1,
min_val=4.5e9 * 2 * np.pi,
max_val=6e9 * 2 * np.pi,
unit="Hz 2pi",
),
"framechange":
Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit="rad"),
}
carr = pulse.Carrier(
name="carrier",
desc="Frequency of the local oscillator",
params=carrier_parameters,
)
carr_2 = copy.deepcopy(carr)
carr_2.params["freq"].set_value(lo_freq_q2)
RX90p_q1 = gates.Instruction(name="RX90p",
t_start=0.0,
t_end=t_final,
channels=["d1"])
RX90p_q2 = gates.Instruction(name="RX90p",
t_start=0.0,
t_end=t_final,
channels=["d2"])
QId_q1 = gates.Instruction(name="Id",
t_start=0.0,
t_end=t_final,
channels=["d1"])
QId_q2 = gates.Instruction(name="Id",
t_start=0.0,
t_end=t_final,
channels=["d2"])
RX90p_q1.add_component(gauss_env_single, "d1")
RX90p_q1.add_component(carr, "d1")
QId_q1.add_component(nodrive_env, "d1")
QId_q1.add_component(copy.deepcopy(carr), "d1")
QId_q1.comps["d1"]["carrier"].params["framechange"].set_value(
(-sideband * t_final) % (2 * np.pi))
Y90p_q1 = copy.deepcopy(RX90p_q1)
Y90p_q1.name = "RY90p"
X90m_q1 = copy.deepcopy(RX90p_q1)
X90m_q1.name = "RX90m"
Y90m_q1 = copy.deepcopy(RX90p_q1)
Y90m_q1.name = "RY90m"
Y90p_q1.comps["d1"]["gauss"].params["xy_angle"].set_value(0.5 * np.pi)
X90m_q1.comps["d1"]["gauss"].params["xy_angle"].set_value(np.pi)
Y90m_q1.comps["d1"]["gauss"].params["xy_angle"].set_value(1.5 * np.pi)
Q1_gates = [QId_q1, RX90p_q1, Y90p_q1, X90m_q1, Y90m_q1]
RX90p_q2.add_component(copy.deepcopy(gauss_env_single), "d2")
RX90p_q2.add_component(carr_2, "d2")
QId_q2.add_component(copy.deepcopy(nodrive_env), "d2")
QId_q2.add_component(copy.deepcopy(carr_2), "d2")
QId_q2.comps["d2"]["carrier"].params["framechange"].set_value(
(-sideband * t_final) % (2 * np.pi))
Y90p_q2 = copy.deepcopy(RX90p_q2)
Y90p_q2.name = "RY90p"
X90m_q2 = copy.deepcopy(RX90p_q2)
X90m_q2.name = "RX90m"
Y90m_q2 = copy.deepcopy(RX90p_q2)
Y90m_q2.name = "RY90m"
Y90p_q2.comps["d2"]["gauss"].params["xy_angle"].set_value(0.5 * np.pi)
X90m_q2.comps["d2"]["gauss"].params["xy_angle"].set_value(np.pi)
Y90m_q2.comps["d2"]["gauss"].params["xy_angle"].set_value(1.5 * np.pi)
Q2_gates = [QId_q2, RX90p_q2, Y90p_q2, X90m_q2, Y90m_q2]
all_1q_gates_comb = []
for g1 in Q1_gates:
for g2 in Q2_gates:
g = gates.Instruction(name="NONE",
t_start=0.0,
t_end=t_final,
channels=[])
g.name = g1.name + ":" + g2.name
channels = []
channels.extend(g1.comps.keys())
channels.extend(g2.comps.keys())
for chan in channels:
g.comps[chan] = {}
if chan in g1.comps:
g.comps[chan].update(g1.comps[chan])
if chan in g2.comps:
g.comps[chan].update(g2.comps[chan])
all_1q_gates_comb.append(g)
for gate in all_1q_gates_comb:
gateset.add_instruction(gate)
# ### MAKE EXPERIMENT
exp = Exp(model=model, generator=generator, gateset=gateset)
return exp
zero_ones[0] = 0
val1 = one_zeros * m00_q1 + zero_ones * m01_q1
val2 = one_zeros * m00_q2 + zero_ones * m01_q2
min_val = one_zeros * 0.8 + zero_ones * 0.0
max_val = one_zeros * 1.0 + zero_ones * 0.2
confusion_row1 = Qty(value=val1, min_val=min_val, max_val=max_val, unit="")
confusion_row2 = Qty(value=val2, min_val=min_val, max_val=max_val, unit="")
conf_matrix = tasks.ConfusionMatrix(Q1=confusion_row1, Q2=confusion_row2)
init_temp = 50e-3
init_ground = tasks.InitialiseGround(
init_temp=Qty(value=init_temp, min_val=-0.001, max_val=0.22, unit="K"))
model = Mdl(
[q1, q2], # Individual, self-contained components
[drive, drive2, q1q2], # Interactions between components
[conf_matrix, init_ground], # SPAM processing
)
model.set_lindbladian(False)
model.set_dressed(True)
sim_res = 100e9 # Resolution for numerical simulation
awg_res = 2e9 # Realistic, limited resolution of an AWG
generator = Gnr(
devices={
"LO":
devices.LO(name="lo", resolution=sim_res, outputs=1),
"AWG":
devices.AWG(name="awg", resolution=awg_res, outputs=1),
# 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 create_experiment():
lindblad = False
dressed = True
qubit_lvls = 3
freq = 5e9
anhar = -210e6
init_temp = 0
qubit_temp = 0
t_final = 7e-9 # Time for single qubit gates
sim_res = 100e9
awg_res = 2e9
sideband = 50e6
lo_freq = 5e9 + sideband
# ### MAKE MODEL
q1 = chip.Qubit(
name="Q1",
desc="Qubit 1",
freq=Qty(
value=freq,
min_val=4.995e9,
max_val=5.005e9,
unit="Hz 2pi",
),
anhar=Qty(
value=anhar,
min_val=-380e6,
max_val=-120e6,
unit="Hz 2pi",
),
hilbert_dim=qubit_lvls,
temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit="K"),
)
drive = chip.Drive(
name="d1",
desc="Drive 1",
comment="Drive line 1 on qubit 1",
connected=["Q1"],
hamiltonian_func=hamiltonians.x_drive,
)
phys_components = [q1]
line_components = [drive]
init_ground = tasks.InitialiseGround(
init_temp=Qty(value=init_temp, min_val=-0.001, max_val=0.22, unit="K"))
task_list = [init_ground]
model = Mdl(phys_components, line_components, task_list)
model.set_lindbladian(lindblad)
model.set_dressed(dressed)
# ### MAKE GENERATOR
generator = Gnr(
devices={
"LO":
devices.LO(name="lo", resolution=sim_res, outputs=1),
"AWG":
devices.AWG(name="awg", resolution=awg_res, outputs=1),
"DigitalToAnalog":
devices.DigitalToAnalog(name="dac",
resolution=sim_res,
inputs=1,
outputs=1),
"Response":
devices.Response(
name="resp",
rise_time=Qty(value=0.3e-9,
min_val=0.05e-9,
max_val=0.6e-9,
unit="s"),
resolution=sim_res,
inputs=1,
outputs=1,
),
"Mixer":
devices.Mixer(name="mixer", inputs=2, outputs=1),
"VoltsToHertz":
devices.VoltsToHertz(
name="v_to_hz",
V_to_Hz=Qty(value=1e9,
min_val=0.9e9,
max_val=1.1e9,
unit="Hz/V"),
inputs=1,
outputs=1,
),
},
chains={
"d1": {
"LO": [],
"AWG": [],
"DigitalToAnalog": ["AWG"],
"Response": ["DigitalToAnalog"],
"Mixer": ["LO", "Response"],
"VoltsToHertz": ["Mixer"],
}
},
)
generator.devices["AWG"].enable_drag_2()
# ### MAKE GATESET
gauss_params_single = {
"amp":
Qty(value=0.45, min_val=0.4, max_val=0.6, unit="V"),
"t_final":
Qty(value=t_final,
min_val=0.5 * t_final,
max_val=1.5 * t_final,
unit="s"),
"sigma":
Qty(value=t_final / 4,
min_val=t_final / 8,
max_val=t_final / 2,
unit="s"),
"xy_angle":
Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit="rad"),
"freq_offset":
Qty(
value=-sideband - 0.5e6,
min_val=-60 * 1e6,
max_val=-40 * 1e6,
unit="Hz 2pi",
),
"delta":
Qty(value=-1, min_val=-5, max_val=3, unit=""),
}
gauss_env_single = pulse.Envelope(
name="gauss",
desc="Gaussian comp for single-qubit gates",
params=gauss_params_single,
shape=envelopes.gaussian_nonorm,
)
nodrive_env = pulse.Envelope(
name="no_drive",
params={
"t_final":
Qty(value=t_final,
min_val=0.5 * t_final,
max_val=1.5 * t_final,
unit="s")
},
shape=envelopes.no_drive,
)
carrier_parameters = {
"freq":
Qty(
value=lo_freq,
min_val=4.5e9,
max_val=6e9,
unit="Hz 2pi",
),
"framechange":
Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit="rad"),
}
carr = pulse.Carrier(
name="carrier",
desc="Frequency of the local oscillator",
params=carrier_parameters,
)
rx90p = gates.Instruction(name="rx90p",
t_start=0.0,
t_end=t_final,
channels=["d1"],
targets=[0])
QId = gates.Instruction(name="id",
t_start=0.0,
t_end=t_final,
channels=["d1"],
targets=[0])
rx90p.add_component(gauss_env_single, "d1")
rx90p.add_component(carr, "d1")
QId.add_component(nodrive_env, "d1")
QId.add_component(copy.deepcopy(carr), "d1")
QId.comps["d1"]["carrier"].params["framechange"].set_value(
(-sideband * t_final) % (2 * np.pi))
ry90p = copy.deepcopy(rx90p)
ry90p.name = "ry90p"
rx90m = copy.deepcopy(rx90p)
rx90m.name = "rx90m"
ry90m = copy.deepcopy(rx90p)
ry90m.name = "ry90m"
ry90p.comps["d1"]["gauss"].params["xy_angle"].set_value(0.5 * np.pi)
rx90m.comps["d1"]["gauss"].params["xy_angle"].set_value(np.pi)
ry90m.comps["d1"]["gauss"].params["xy_angle"].set_value(1.5 * np.pi)
parameter_map = PMap(instructions=[QId, rx90p, ry90p, rx90m, ry90m],
model=model,
generator=generator)
# ### MAKE EXPERIMENT
exp = Exp(pmap=parameter_map)
return exp
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)
drive_q2 = chip.Drive(
name="Q2",
desc="Drive on Q2",
connected=["Qubit2"],
hamiltonian_func=hamiltonians.x_drive,
)
flux = chip.Drive(
name="TC",
desc="Flux drive/control on tunable couler",
connected=["TCQubit"],
hamiltonian_func=hamiltonians.z_drive,
)
phys_components = [tc_at, q1, q2]
line_components = [q1tc, q2tc, q1q2, drive_q1, drive_q2, flux]
model = Mdl(phys_components, line_components, [])
model.set_lindbladian(lindblad)
model.set_dressed(dressed)
# ### 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.Response(
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")
fluxbias = devices.FluxTuning(
name="fluxbias",
