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 _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}
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
# TODO: Check full hamiltonian
@pytest.mark.unit
def test_three_wave_mixer_properties() -> None:
"Test if the values of the three wave mixer element are assigned correctly"