from dataclasses import asdict, dataclass, field
import numpy as np
import numpy.typing as npt
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from qibolab import AcquisitionType, AveragingMode, ExecutionParameters
from qibolab.platform import Platform
from qibolab.pulses import PulseSequence
from qibolab.qubits import QubitId
from qibolab.sweeper import Parameter, Sweeper, SweeperType
from qibocal import update
from qibocal.auto.operation import Data, Parameters, Results, Routine
from qibocal.protocols.utils import (
HZ_TO_GHZ,
lorentzian,
lorentzian_fit,
table_dict,
table_html,
)
[docs]@dataclass
class DispersiveShiftParameters(Parameters):
"""Dispersive shift inputs."""
freq_width: int
"""Width [Hz] for frequency sweep relative to the readout frequency [Hz]."""
freq_step: int
"""Frequency step for sweep [Hz]."""
[docs]@dataclass
class DispersiveShiftResults(Results):
"""Dispersive shift outputs."""
frequency_state_zero: dict[QubitId, float]
"""State zero frequency."""
frequency_state_one: dict[QubitId, float]
"""State one frequency."""
fitted_parameters_state_zero: dict[QubitId, list[float]]
"""Fitted parameters state zero."""
fitted_parameters_state_one: dict[QubitId, list[float]]
"""Fitted parameters state one."""
best_freq: dict[QubitId, float]
"""Readout frequency that maximizes the distance of ground and excited states in iq-plane"""
@property
def state_zero(self):
return {key: value for key, value in asdict(self).items() if "zero" in key}
@property
def state_one(self):
return {key: value for key, value in asdict(self).items() if "one" in key}
DispersiveShiftType = np.dtype(
[
("freq", np.float64),
("i", np.float64),
("q", np.float64),
("signal", np.float64),
("phase", np.float64),
]
)
"""Custom dtype for dispersive shift."""
[docs]@dataclass
class DispersiveShiftData(Data):
"""Dispersive shift acquisition outputs."""
resonator_type: str
"""Resonator type."""
data: dict[tuple[QubitId, int], npt.NDArray[DispersiveShiftType]] = field(
default_factory=dict
)
[docs]def _acquisition(
params: DispersiveShiftParameters, platform: Platform, targets: list[QubitId]
) -> DispersiveShiftData:
r"""
Data acquisition for dispersive shift experiment.
Perform spectroscopy on the readout resonator, with the qubit in ground and excited state, showing
the resonator shift produced by the coupling between the resonator and the qubit.
Args:
params (DispersiveShiftParameters): experiment's parameters
platform (Platform): Qibolab platform object
targets (list): list of target qubits to perform the action
"""
# create 2 sequences of pulses for the experiment:
# sequence_0: I - MZ
# sequence_1: RX - MZ
# taking advantage of multiplexing, apply the same set of gates to all qubits in parallel
sequence_0 = PulseSequence()
sequence_1 = PulseSequence()
ro_pulses = {}
qd_pulses = {}
for qubit in targets:
qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0)
ro_pulses[qubit] = platform.create_qubit_readout_pulse(
qubit, start=qd_pulses[qubit].duration
)
sequence_0.add(ro_pulses[qubit])
sequence_1.add(qd_pulses[qubit])
sequence_1.add(ro_pulses[qubit])
# define the parameter to sweep and its range:
delta_frequency_range = np.arange(
-params.freq_width / 2, params.freq_width / 2, params.freq_step
)
# create a DataUnits objects to store the results
data = DispersiveShiftData(resonator_type=platform.resonator_type)
sweeper = Sweeper(
Parameter.frequency,
delta_frequency_range,
pulses=[ro_pulses[qubit] for qubit in targets],
type=SweeperType.OFFSET,
)
execution_pars = ExecutionParameters(
nshots=params.nshots,
relaxation_time=params.relaxation_time,
acquisition_type=AcquisitionType.INTEGRATION,
averaging_mode=AveragingMode.CYCLIC,
)
results_0 = platform.sweep(
sequence_0,
execution_pars,
sweeper,
)
results_1 = platform.sweep(
sequence_1,
execution_pars,
sweeper,
)
# retrieve the results for every qubit
for qubit in targets:
for i, results in enumerate([results_0, results_1]):
result = results[ro_pulses[qubit].serial].average
# store the results
data.register_qubit(
DispersiveShiftType,
(qubit, i),
dict(
freq=ro_pulses[qubit].frequency + delta_frequency_range,
signal=result.magnitude,
phase=result.phase,
i=result.voltage_i,
q=result.voltage_q,
),
)
return data
[docs]def _fit(data: DispersiveShiftData) -> DispersiveShiftResults:
"""Post-Processing for dispersive shift"""
qubits = data.qubits
iq_couples = [[], []] # axis 0: states, axis 1: qubit
frequency_0 = {}
frequency_1 = {}
best_freqs = {}
fitted_parameters_0 = {}
fitted_parameters_1 = {}
for i in range(2):
for qubit in qubits:
data_i = data[qubit, i]
fit_result = lorentzian_fit(
data_i, resonator_type=data.resonator_type, fit="resonator"
)
if fit_result is not None:
if i == 0:
frequency_0[qubit], fitted_parameters_0[qubit], _ = fit_result
else:
frequency_1[qubit], fitted_parameters_1[qubit], _ = fit_result
i_measures = data_i.i
q_measures = data_i.q
iq_couples[i].append(np.stack((i_measures, q_measures), axis=-1))
# for each qubit find the iq couple of 0-1 states that maximize the distance
iq_couples = np.array(iq_couples)
for idx, qubit in enumerate(qubits):
frequencies = data[qubit, 0].freq
max_index = np.argmax(
np.linalg.norm(iq_couples[0][idx] - iq_couples[1][idx], axis=-1)
)
best_freqs[qubit] = frequencies[max_index]
return DispersiveShiftResults(
frequency_state_zero=frequency_0,
frequency_state_one=frequency_1,
fitted_parameters_state_one=fitted_parameters_1,
fitted_parameters_state_zero=fitted_parameters_0,
best_freq=best_freqs,
)
[docs]def _plot(data: DispersiveShiftData, target: QubitId, fit: DispersiveShiftResults):
"""Plotting function for dispersive shift."""
figures = []
fig = make_subplots(
rows=1,
cols=2,
horizontal_spacing=0.1,
vertical_spacing=0.1,
subplot_titles=(
"Signal [a.u.]",
"phase [rad]",
),
)
# iterate over multiple data folders
fitting_report = ""
data_0 = data[target, 0]
data_1 = data[target, 1]
fit_data_0 = fit.state_zero if fit is not None else None
fit_data_1 = fit.state_one if fit is not None else None
for i, label, q_data, data_fit in list(
zip(
(0, 1),
("State 0", "State 1"),
(data_0, data_1),
(fit_data_0, fit_data_1),
)
):
opacity = 1
frequencies = q_data.freq * HZ_TO_GHZ
fig.add_trace(
go.Scatter(
x=frequencies,
y=q_data.signal,
opacity=opacity,
name=f"{label}",
showlegend=True,
legendgroup=f"{label}",
),
row=1,
col=1,
)
fig.add_trace(
go.Scatter(
x=frequencies,
y=q_data.phase,
opacity=opacity,
showlegend=False,
legendgroup=f"{label}",
),
row=1,
col=2,
)
if fit is not None:
freqrange = np.linspace(
min(frequencies),
max(frequencies),
2 * len(q_data),
)
params = data_fit[
(
"fitted_parameters_state_zero"
if i == 0
else "fitted_parameters_state_one"
)
][target]
fig.add_trace(
go.Scatter(
x=freqrange,
y=lorentzian(freqrange, *params),
name=f"{label} Fit",
line=go.scatter.Line(dash="dot"),
),
row=1,
col=1,
)
if fit is not None:
fig.add_trace(
go.Scatter(
x=[
fit.best_freq[target] * HZ_TO_GHZ,
fit.best_freq[target] * HZ_TO_GHZ,
],
y=[
np.min(np.concatenate((data_0.signal, data_1.signal))),
np.max(np.concatenate((data_0.signal, data_1.signal))),
],
mode="lines",
line=go.scatter.Line(color="orange", width=3, dash="dash"),
name="Best frequency",
),
row=1,
col=1,
)
fig.add_vline(
x=fit.best_freq[target] * HZ_TO_GHZ,
line=dict(color="orange", width=3, dash="dash"),
row=1,
col=1,
)
fitting_report = table_html(
table_dict(
target,
[
"State Zero Frequency [Hz]",
"State One Frequency [Hz]",
"Chi [Hz]",
"Best Frequency [Hz]",
],
np.round(
[
fit_data_0["frequency_state_zero"][target],
fit_data_1["frequency_state_one"][target],
(
fit_data_0["frequency_state_zero"][target]
- fit_data_1["frequency_state_one"][target]
)
/ 2,
fit.best_freq[target],
]
),
)
)
fig.update_layout(
showlegend=True,
xaxis_title="Frequency [GHz]",
yaxis_title="Signal [a.u.]",
xaxis2_title="Frequency [GHz]",
yaxis2_title="Phase [rad]",
)
figures.append(fig)
return figures, fitting_report
[docs]def _update(results: DispersiveShiftResults, platform: Platform, target: QubitId):
update.readout_frequency(results.best_freq[target], platform, target)
dispersive_shift = Routine(_acquisition, _fit, _plot, _update)
"""Dispersive shift Routine object."""