---

Download

Download this file as Jupyter notebook: calibration_set.ipynb.

Managing calibration data and linkers

Note

This guide requires the asset calibration set.

In this guide, we show how to use a CalibrationSet to manage calibration data and the operation definitions provided by Linkerss. The CalibrationSet object provides a way to store and update calibration data across multiple objects simultaneously. The CalibrationSet used in this and other guides has the parameters listed below.

We work with Linkers for single-qubit \(XY\) rotations and multi-qubit cross-resonance gates, both common in superconducting systems. See Compiling single-qubit native gates to waveforms, Compiling multi-qubit native gates to waveforms, and Compiling measurements to waveforms and acquisitions to learn how to create these objects in detail.

Native gates to compile

We first define the native gates we want to compile, represented by the following ParametricGates:

import keysight.qcs as qcs
import numpy as np

# euler decomposition for XY rotations
layers = [-qcs.PAULIS.sigma_z, qcs.PAULIS.sigma_x, qcs.PAULIS.sigma_z]
zxz = qcs.ParametricGate(layers, ["phi", "theta", "phi"])

# cross-resonance multi-qubit gate
zx_ham = qcs.PAULIS.sigma_z & qcs.PAULIS.sigma_x
cr = qcs.ParametricGate([zx_ham], ["beta"])

Anatomy of a CalibrationSet

A CalibrationSet contains a VariableSet and a dictionary of Linkerss. We load one with single- and multi-qubit linkers for our native gates.

calibration_set = qcs.load("../../assets/calibration.qcs")

Variables

The calibration variables can be accessed via variables.

calibration_set.variable_list

[Array(name=single_qubit_frequencies, shape=(4,), dtype=float, unit=Hz),
 Array(name=single_qubit_amplitudes, shape=(4,), dtype=float, unit=none),
 Array(name=multi_qubit_amplitudes, shape=(2,), dtype=float, unit=none),
 Scalar(name=single_qubit_duration, value=3e-08, dtype=float, unit=s),
 Scalar(name=multi_qubit_duration, value=3e-08, dtype=float, unit=s),
 Array(name=readout_frequencies, shape=(4,), dtype=float, unit=Hz),
 Scalar(name=readout_frequency_detuning, value=0.0, dtype=float, unit=Hz),
 Array(name=readout_amplitudes, shape=(4,), dtype=float, unit=none),
 Array(name=readout_phases, shape=(4,), dtype=float, unit=rad),
 Scalar(name=readout_plateau_time, value=1e-07, dtype=float, unit=s),
 Scalar(name=readout_rise_time, value=3e-08, dtype=float, unit=s),
 Scalar(name=readout_pulse_delay, value=4e-08, dtype=float, unit=s),
 Scalar(name=acquisition_duration, value=2e-07, dtype=float, unit=s),
 Array(name=ref_pts, shape=(4, 2), dtype=complex, unit=none),
 Array(name=phis, shape=(4,), dtype=float, unit=rad),
 Array(name=thetas, shape=(4,), dtype=float, unit=none),
 Array(name=beta, shape=(2,), dtype=float, unit=none),
 Scalar(name=alpha, value=0.33, dtype=float, unit=none)]

While it is convenient to save the full calibration set with variables and linkers in a single file, it is also possible to save the variables separately using the export_values() method, which creates a json file that can easily be edited.

This can be integrated into a workflow where calibration variables can be saved at certain times to track their history.

# calibration_set.export_values("calibration_values_Jan_01_2024.qcs")

# the variables stored in this format are sorted by qudit:
print(open("calibration_values_Jan_01_2024.qcs").read())

{
  "qudits_0": {
    "phis": 0.0,
    "thetas": 0.0,
    "single_qubit_duration": 3E-08,
    "single_qubit_amplitudes": 0.1,
    "single_qubit_frequencies": 5000000000.0,
    "readout_pulse_delay": 4E-08,
    "readout_rise_time": 3E-08,
    "readout_amplitudes": 0.1,
    "readout_frequencies": 5800000000.0,
    "readout_frequency_detuning": 0.0,
    "readout_phases": 0.0,
    "readout_plateau_time": 1E-07,
    "acquisition_duration": 2E-07,
    "ref_pts": {
      "real": [
        1.0,
        1.0
      ],
      "imag": [
        1.0,
        -1.0
      ]
    }
  },
  "qudits_1": {
    "phis": 0.0,
    "thetas": 0.0,
    "single_qubit_duration": 3E-08,
    "single_qubit_amplitudes": 0.1,
    "single_qubit_frequencies": 5100000000.0,
    "readout_pulse_delay": 4E-08,
    "readout_rise_time": 3E-08,
    "readout_amplitudes": 0.1,
    "readout_frequencies": 5850000000.0,
    "readout_frequency_detuning": 0.0,
    "readout_phases": 0.0,
    "readout_plateau_time": 1E-07,
    "acquisition_duration": 2E-07,
    "ref_pts": {
      "real": [
        1.0,
        1.0
      ],
      "imag": [
        1.0,
        -1.0
      ]
    }
  },
  "qudits_2": {
    "phis": 0.0,
    "thetas": 0.0,
    "single_qubit_duration": 3E-08,
    "single_qubit_amplitudes": 0.1,
    "single_qubit_frequencies": 5200000000.0,
    "readout_pulse_delay": 4E-08,
    "readout_rise_time": 3E-08,
    "readout_amplitudes": 0.1,
    "readout_frequencies": 5900000000.0,
    "readout_frequency_detuning": 0.0,
    "readout_phases": 0.0,
    "readout_plateau_time": 1E-07,
    "acquisition_duration": 2E-07,
    "ref_pts": {
      "real": [
        1.0,
        1.0
      ],
      "imag": [
        1.0,
        -1.0
      ]
    }
  },
  "qudits_3": {
    "phis": 0.0,
    "thetas": 0.0,
    "single_qubit_duration": 3E-08,
    "single_qubit_amplitudes": 0.1,
    "single_qubit_frequencies": 5300000000.0,
    "readout_pulse_delay": 4E-08,
    "readout_rise_time": 3E-08,
    "readout_amplitudes": 0.1,
    "readout_frequencies": 5950000000.0,
    "readout_frequency_detuning": 0.0,
    "readout_phases": 0.0,
    "readout_plateau_time": 1E-07,
    "acquisition_duration": 2E-07,
    "ref_pts": {
      "real": [
        1.0,
        1.0
      ],
      "imag": [
        1.0,
        -1.0
      ]
    }
  },
  "couplings_0": {
    "beta": 0.0,
    "multi_qubit_duration": 3E-08,
    "multi_qubit_amplitudes": 0.1,
    "single_qubit_frequencies": 5000000000.0,
    "alpha": 0.33
  },
  "couplings_1": {
    "beta": 0.0,
    "multi_qubit_duration": 3E-08,
    "multi_qubit_amplitudes": 0.1,
    "single_qubit_frequencies": 5100000000.0,
    "alpha": 0.33
  }
}

We can edit the exported file and load the edits back in using the import_values() method:

calibration_set.import_values("calibration_values_Jan_01_2024.qcs")

Linkers

The dictionary of linkers can be accessed via linkers().

list(calibration_set.linkers.keys())

['single_qubit_linker', 'multi_qubit_linker_suppression', 'measurement_linker']

A CalibrationSet also has an active_linkers property that can be used to specify a subset of all available linkers. By default, all linkers are active and can be set using their names.

calibration_set.active_linkers = ["single_qubit_linker", "measurement_linker"]
list(calibration_set.active_linkers.keys())

['single_qubit_linker', 'measurement_linker']

Using the Linkers

Now, using the linkers within, we can define gate-level programs and compile them:

qubits = qcs.Qudits(range(2))

# define a sample program
program_to_compile = qcs.Program()

# apply X90 on qubit 0 and an X on qubit 1
phis = qcs.Array("phis", value=[0.0, 0.0])
thetas = qcs.Array("thetas", value=[np.pi / 2, np.pi])
program_to_compile.add_parametric_gate(zxz, [phis, thetas], qubits)

# apply (ZX)^1/2 on qubits (0, 1)
beta = qcs.Array("beta", value=[np.pi / 2])
multiqudits = qcs.MultiQudits.from_qudits((qubits[0], qubits[1]))
program_to_compile.add_parametric_gate(cr, [beta], multiqudits)

program_to_compile.draw()

Program

Program Body Program Duration undefined Layers 2 Targets 2 Layer #0 Layer #0 Duration undefined Layer #1 Layer #1 Duration undefined qudits 0 PGATE ParameterizedGate on ('qudits', 0) Parameters phi, theta, phi Values Array(name=phis, shape=(2,), dtype=float, unit=none) Array(name=thetas, shape=(2,), dtype=float, unit=none) Matrices -1 0 0 1 0 1 1 0 1 0 0 -1 MULTIGATE ParameterizedGate on (('qudits', 0), ('qudits', 1)) Parameters beta Values ScalarRef(name=beta, value=1.5707963267948966, dtype=float, unit=none) Matrices 0 1 0 0 1 0 0 0 0 0 0 -1 0 0 -1 0 1 PGATE ParameterizedGate on ('qudits', 1) Parameters phi, theta, phi Values Array(name=phis, shape=(2,), dtype=float, unit=none) Array(name=thetas, shape=(2,), dtype=float, unit=none) Matrices -1 0 0 1 0 1 1 0 1 0 0 -1 MULTIGATE ParameterizedGate on (('qudits', 1), ('qudits', 0)) Parameters beta Values ScalarRef(name=beta, value=1.5707963267948966, dtype=float, unit=none) Matrices 0 1 0 0 1 0 0 0 0 0 0 -1 0 0 -1 0

Next, we define a LinkerPass to compile the program:

linker_pass = qcs.LinkerPass(*calibration_set.linkers.values())
program_compiled = linker_pass.apply(program_to_compile)
program_compiled.draw()

Program

Program Body Program Duration 60 ns Layers 2 Targets 2 Layer #0 Layer #0 Duration 30 ns Layer #1 Layer #1 Duration 30 ns xy_channels 0 RFWaveform on ('xy_channels', 0) Parameters Duration Scalar(name=single_qubit_duration, value=30 ns, dtype=float, unit=s) Amplitude ArraySlice(name=_implicit, shape=(2,), dtype=float, unit=none, value=[0.1571, 0.3142]) Frequency ArraySlice(name=single_qubit_frequencies, shape=(2,), dtype=float, unit=Hz, value=[5 GHz, 5.1 GHz]) Envelope GaussianEnvelope(2.0) Instantaneous Phase ArraySlice(name=_implicit, shape=(2,), dtype=float, unit=rad, value=[0 rad, 0 rad]) Post-phase 0 rad RFWaveform on ('xy_channels', 0) Parameters Duration Scalar(name=multi_qubit_duration, value=30 ns, dtype=float, unit=s) Amplitude ScalarRef(name=_implicit, value=0.1571, dtype=float, unit=none) Frequency ScalarRef(name=single_qubit_frequencies, value=5.1 GHz, dtype=float, unit=Hz) Envelope GaussianEnvelope(2.0) Instantaneous Phase 0 rad Post-phase 0 rad 1 RFWaveform on ('xy_channels', 1) Parameters Duration Scalar(name=single_qubit_duration, value=30 ns, dtype=float, unit=s) Amplitude ArraySlice(name=_implicit, shape=(2,), dtype=float, unit=none, value=[0.1571, 0.3142]) Frequency ArraySlice(name=single_qubit_frequencies, shape=(2,), dtype=float, unit=Hz, value=[5 GHz, 5.1 GHz]) Envelope GaussianEnvelope(2.0) Instantaneous Phase ArraySlice(name=_implicit, shape=(2,), dtype=float, unit=rad, value=[0 rad, 0 rad]) Post-phase 0 rad RFWaveform on ('xy_channels', 1) Parameters Duration Scalar(name=multi_qubit_duration, value=30 ns, dtype=float, unit=s) Amplitude ScalarRef(name=_implicit, value=0.0518, dtype=float, unit=none) Frequency ScalarRef(name=single_qubit_frequencies, value=5.1 GHz, dtype=float, unit=Hz) Envelope GaussianEnvelope(2.0) Instantaneous Phase 0 rad Post-phase 0 rad

As this program now only contains waveforms, we render it:

program_compiled.render(
    channel_subplots=False,
    lo_frequency=5e9,
    sample_rate=qcs.SAMPLE_RATES[qcs.InstrumentEnum.M5300AWG],
)

Updating calibration data

We can update the values of the variables in the CalibrationSet to update all the variables in Linkerss that use them. Since the cross-resonance gate uses the single-qubit frequencies, both waveforms are updated to reflect the new value.

calibration_set.variables.single_qubit_frequencies.value[1] += 100e6

program_compiled.render(
    channel_subplots=False,
    lo_frequency=5e9,
    sample_rate=qcs.SAMPLE_RATES[qcs.InstrumentEnum.M5300AWG],
)

Adding new linkers

Finally, we can create new linkers with the variables in and add new variables to the calibration set, then use the add_linker() method to so it can be saved, loaded, and updated across multiple sessions.

operation = calibration_set.linkers["multi_qubit_linker_suppression"].operation
targets = calibration_set.linkers["multi_qubit_linker_suppression"].targets

mq_amp_cals = calibration_set.variables.multi_qubit_amplitudes
frequency_cals = calibration_set.variables.single_qubit_frequencies

# # define a new duration for this gate
mq_dur = qcs.Scalar("multi_qubit_duration_new", value=20e-9, dtype=float)
calibration_set.variables.declare(mq_dur)

gamma = qcs.Array(name="gamma", shape=mq_amp_cals.shape, dtype=float)

prog = qcs.Program()
channels = qcs.Channels([0, 2], "xy_channels")
envelope = qcs.GaussianEnvelope()
pulse = qcs.RFWaveform(mq_dur, envelope, gamma * mq_amp_cals, frequency_cals[1::2])

prog.add_waveform(pulse, channels)

calibration_set.add_linker(
    "multi_qubit_linker", qcs.ParameterizedLinker(operation, targets, prog)
)

---

Download

Download this file as Jupyter notebook: calibration_set.ipynb.