"""CZ virtual correction experiment for two qubit gates, tune landscape."""
from dataclasses import dataclass, field
from typing import Literal, Optional
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,
Delay,
Parameter,
Pulse,
PulseSequence,
Sweeper,
VirtualZ,
)
from qibocal.auto.operation import (
Data,
Parameters,
QubitId,
QubitPairId,
Results,
Routine,
)
from qibocal.calibration import CalibrationPlatform
from qibocal.protocols.utils import table_dict, table_html
from ... import update
from ...update import replace
from .utils import fit_sinusoid, fit_virtualz, order_pair, phase_diff, sinusoid
__all__ = ["correct_virtual_z_phases", "create_sequence", "fit_sinusoid", "phase_diff"]
[docs]
@dataclass
class VirtualZPhasesParameters(Parameters):
"""VirtualZ runcard inputs."""
theta_start: float
"""Initial angle for the low frequency qubit measurement in radians."""
theta_end: float
"""Final angle for the low frequency qubit measurement in radians."""
theta_step: float
"""Step size for the theta sweep in radians."""
native: str = "CZ"
"""Two qubit interaction to be calibrated.
iSWAP and CZ are the possible options.
"""
dt: Optional[float] = 16
"""Time delay between flux pulses and readout."""
gate_repetition: int = 1
"""Number of CZ repetition"""
@dataclass
class VirtualZPhasesResults(Results):
"""VirtualZ outputs when fitting will be done."""
fitted_parameters: dict[tuple[str, QubitId],]
"""Fitted parameters"""
native: str
"""Native two qubit gate."""
gate_repetition: int
leakage: dict[QubitPairId, dict[QubitId, float]]
"""Leakage on control qubit for pair."""
angle: Optional[dict[QubitPairId, float]] = None
"""Native angle."""
virtual_phase: Optional[dict[QubitPairId, dict[QubitId, float]]] = None
"""Virtual Z phase correction."""
def __contains__(self, key: QubitPairId):
"""Check if key is in class.
While key is a QubitPairId both chsh and chsh_mitigated contain
an additional key which represents the basis chosen.
"""
return key in [(target, control) for target, control in self.angle]
VirtualZPhasesType = np.dtype([("target", np.float64), ("control", np.float64)])
@dataclass
class VirtualZPhasesData(Data):
"""VirtualZPhases data."""
gate_repetition: int
data: dict[tuple, npt.NDArray[VirtualZPhasesType]] = field(default_factory=dict)
native: str = "CZ"
thetas: list = field(default_factory=list)
def __getitem__(self, pair):
return {
index: value
for index, value in self.data.items()
if set(pair).issubset(index)
}
@property
def order_pairs(self):
pairs = []
for key in self.data.keys():
pairs.append((key[0], key[1]))
return np.unique(pairs, axis=0).tolist()
[docs]
def create_sequence(
platform: CalibrationPlatform,
setup: Literal["I", "X"],
target_qubit: QubitId,
control_qubit: QubitId,
ordered_pair: list[QubitId, QubitId],
native: Literal["CZ", "iSWAP"],
dt: float,
flux_pulse_max_duration: float = None,
gate_repetition: int = 1,
flux_pulse: Optional[list] = None,
) -> tuple[PulseSequence, Pulse, Pulse, list[Pulse]]:
"""
Create the pulse sequence for the calibration of two-qubit gate virtual phases.
This function constructs a pulse sequence for a given two-qubit native gate `native` (CZ or iSWAP)
on the specified qubits. The sequence includes:
- A preliminary RX90 pulse on the `target_qubit`.
- An optional X pulse on the `control_qubit` based on the `setup` type.
- A flux pulse implementing the two-qubit native gate.
- A delay of duration `dt` before the final X90 pulse on the target qubit.
- Measurement pulses.
It is possible to specify the maximum duration for the flux pulses with the
`flux_pulse_max_duration` parameter.
The function returns:
- The full experiment pulse sequence.
- The applied flux pulse.
- The final `VirtualZPhase` pulses to be used for phase sweeping.
"""
target_natives = platform.natives.single_qubit[target_qubit]
control_natives = platform.natives.single_qubit[control_qubit]
sequence = PulseSequence()
# X90
sequence += target_natives.R(theta=np.pi / 2)
# X
if setup == "X":
sequence += control_natives.RX()
flux_channel = platform.qubits[ordered_pair[1]].flux
# CZ
if flux_pulse is None:
cz_sequence = platform.natives.two_qubit[ordered_pair].CZ
flux_pulse = list(cz_sequence.channel(flux_channel))[0]
if flux_pulse_max_duration is not None:
flux_pulse = replace(flux_pulse, duration=flux_pulse_max_duration)
flux_sequence = PulseSequence([(flux_channel, flux_pulse)])
virtual_phases = []
align_channels = [
platform.qubits[control_qubit].drive,
platform.qubits[target_qubit].drive,
flux_channel,
platform.qubits[target_qubit].acquisition,
platform.qubits[control_qubit].acquisition,
]
sequence.align(align_channels)
for _ in range(gate_repetition):
sequence.append((flux_channel, Delay(duration=dt)))
sequence += flux_sequence
sequence.append((flux_channel, Delay(duration=dt)))
# Instead of having many RZ as expressed in gate_repetition,
# a single RZ with angle (theta*gate_repetition) is added because qm ignores the first one.
# This work for CZ since it commutes with the RZ, but break the iSWAP compatibility.
# See https://github.com/qiboteam/qibolab/discussions/1198.
virtual_phases.append(VirtualZ(phase=0))
sequence.append((platform.qubits[target_qubit].drive, virtual_phases[-1]))
theta_sequence = PulseSequence()
# RX90 (angle to be swept)
sequence.align(align_channels)
theta_sequence += target_natives.R(theta=np.pi / 2)
sequence += theta_sequence
# X gate for the leakage
if setup == "X":
sequence += control_natives.RX()
sequence.align(align_channels)
ro_sequence = PulseSequence(
[
target_natives.MZ()[0],
control_natives.MZ()[0],
]
)
sequence += ro_sequence
return sequence, flux_pulse, virtual_phases
def _acquisition(
params: VirtualZPhasesParameters,
platform: CalibrationPlatform,
targets: list[QubitPairId],
) -> VirtualZPhasesData:
r"""
Acquisition for VirtualZPhases.
Check the two-qubit landscape created by a flux pulse of a given duration
and amplitude.
The system is initialized with a X90 pulse on the low frequency qubit and either
an Id or an X gate on the high frequency qubit. Then the flux pulse is applied to
the high frequency qubit in order to perform a two-qubit interaction.
A $X_{\beta}90$ pulse is applied to the low frequency qubit before measurement.
That is, a pi-half pulse around the relative phase parametereized by the angle theta.
Measurements on the low frequency qubit yield the 2Q-phase of the gate and the
remnant single qubit Z phase aquired during the execution to be corrected.
Population of the high frequency qubit yield the leakage to the non-computational states
during the execution of the flux pulse.
"""
assert params.native == "CZ", "This protocol supports only CZ gate."
theta_absolute = np.arange(params.theta_start, params.theta_end, params.theta_step)
data = VirtualZPhasesData(
gate_repetition=params.gate_repetition,
thetas=theta_absolute.tolist(),
native=params.native,
)
for pair in targets:
# order the qubits so that the low frequency one is the first
ordered_pair = order_pair(pair, platform)
for target_q, control_q in (
(ordered_pair[0], ordered_pair[1]),
(ordered_pair[1], ordered_pair[0]),
):
for setup in ("I", "X"):
(
sequence,
_,
vz_pulses,
) = create_sequence(
platform,
setup,
target_q,
control_q,
ordered_pair,
params.native,
dt=params.dt,
gate_repetition=params.gate_repetition,
)
# The virtual phase values are the opposite of beta, this is
# because, according to the circuit we would like to reproduce
# after the CZ, an RZ is applied. The RZ gate with `theta` angle
# is compiled into a VirtualPhase pulse with phase `-theta`.
# (See https://github.com/qiboteam/qibolab/pull/1044#issuecomment-2354622956)
sweeper = Sweeper(
parameter=Parameter.phase,
range=(
-params.gate_repetition * params.theta_start,
-params.gate_repetition * params.theta_end,
-params.gate_repetition * params.theta_step,
),
pulses=vz_pulses,
)
results = platform.execute(
[sequence],
[[sweeper]],
nshots=params.nshots,
relaxation_time=params.relaxation_time,
acquisition_type=AcquisitionType.DISCRIMINATION,
averaging_mode=AveragingMode.CYCLIC,
)
ro_target = list(
sequence.channel(platform.qubits[target_q].acquisition)
)[-1]
ro_control = list(
sequence.channel(platform.qubits[control_q].acquisition)
)[-1]
result_target = results[ro_target.id]
result_control = results[ro_control.id]
data.register_qubit(
VirtualZPhasesType,
(target_q, control_q, setup),
dict(
target=result_target,
control=result_control,
),
)
return data
def _fit(
data: VirtualZPhasesData,
) -> VirtualZPhasesResults:
r"""Fitting routine for the experiment.
The used model is
.. math::
y = p_0 sin\Big(x + p_2\Big) + p_1.
"""
fitted_parameters = {}
pairs = data.order_pairs
virtual_phase = {}
angle = {}
leakage = {}
for pair in pairs:
new_fitted_parameter, new_phases, new_angle, new_leak = fit_virtualz(
data[pair], tuple(pair), data.thetas, data.gate_repetition
)
fitted_parameters |= new_fitted_parameter
virtual_phase |= new_phases
angle |= new_angle
leakage |= new_leak
return VirtualZPhasesResults(
native=data.native,
gate_repetition=data.gate_repetition,
angle=angle,
virtual_phase=virtual_phase,
fitted_parameters=fitted_parameters,
leakage=leakage,
)
def _plot(data: VirtualZPhasesData, fit: VirtualZPhasesResults, target: QubitPairId):
"""Plot routine for VirtualZPhases."""
pair_data = data[target]
qubits = next(iter(pair_data))[:2]
fig1 = make_subplots(
rows=1,
cols=2,
subplot_titles=(
f"Qubit {qubits[0]}",
f"Qubit {qubits[1]}",
),
)
fitting_report = set()
fig2 = make_subplots(
rows=1,
cols=2,
subplot_titles=(
f"Qubit {qubits[0]}",
f"Qubit {qubits[1]}",
),
)
thetas = data.thetas
for target_q, control_q, setup in pair_data:
target_prob = pair_data[target_q, control_q, setup].target
control_prob = pair_data[target_q, control_q, setup].control
fig = fig1 if (target_q, control_q) == qubits else fig2
fig.add_trace(
go.Scatter(
x=np.array(thetas),
y=target_prob,
name=f"{setup} sequence",
legendgroup=setup,
),
row=1,
col=1 if fig == fig1 else 2,
)
fig.add_trace(
go.Scatter(
x=np.array(thetas),
y=control_prob,
name=f"{setup} sequence",
legendgroup=setup,
),
row=1,
col=2 if fig == fig1 else 1,
)
if fit is not None and setup == "I":
angle_range = np.linspace(thetas[0], thetas[-1], 100)
fitted_parameters = fit.fitted_parameters[(target_q, control_q), setup]
fig.add_trace(
go.Scatter(
x=angle_range,
y=sinusoid(
angle_range,
data.gate_repetition,
*fitted_parameters,
),
name="Fit",
line=go.scatter.Line(dash="dot"),
),
row=1,
col=1 if fig == fig1 else 2,
)
fitting_report.add(
table_html(
table_dict(
[target_q, target_q, control_q],
[
f"{fit.native} angle [rad]",
"Virtual Z phase [rad]",
"Leakage [a.u.]",
],
[
np.round(fit.angle[target_q, control_q], 4),
np.round(
fit.virtual_phase[target_q, control_q],
4,
),
np.round(fit.leakage[target_q, control_q], 4),
],
)
)
)
fig1.update_layout(
title_text=f"Phase correction Qubit {qubits[0]}",
showlegend=True,
xaxis1_title="Virtual phase[rad]",
xaxis2_title="Virtual phase [rad]",
yaxis_title="State 1 Probability",
)
fig2.update_layout(
title_text=f"Phase correction Qubit {qubits[1]}",
showlegend=True,
xaxis1_title="Virtual phase[rad]",
xaxis2_title="Virtual phase[rad]",
yaxis_title="State 1 Probability",
)
return [fig1, fig2], "".join(fitting_report) # target and control qubit
def _update(
results: VirtualZPhasesResults, platform: CalibrationPlatform, target: QubitPairId
):
if results.gate_repetition == 1:
# FIXME: quick fix for qubit order
target = tuple(sorted(target))
update.virtual_phases(results.virtual_phase, results.native, platform, target)
correct_virtual_z_phases = Routine(
_acquisition, _fit, _plot, _update, two_qubit_gates=True
)
"""Virtual phases correction protocol."""