Frequency Sweep Examples in LabOne Q¶

This notebook demonstrates various types of frequency sweeps in LabOne Q, including linear and non-linear sweeps.

In [ ]:

import matplotlib.pyplot as plt
import numpy as np

from laboneq.contrib.example_helpers.generate_device_setup import (
    generate_device_setup_qubits,
)
from laboneq.contrib.example_helpers.plotting.plot_helpers import plot_simulation
from laboneq.core.types.enums import ModulationType
from laboneq.dsl.calibration import Oscillator, SignalCalibration
from laboneq.dsl.experiment import pulse_library
from laboneq.simple import *

import matplotlib.pyplot as plt import numpy as np from laboneq.contrib.example_helpers.generate_device_setup import ( generate_device_setup_qubits, ) from laboneq.contrib.example_helpers.plotting.plot_helpers import plot_simulation from laboneq.core.types.enums import ModulationType from laboneq.dsl.calibration import Oscillator, SignalCalibration from laboneq.dsl.experiment import pulse_library from laboneq.simple import *

In [ ]:

device_setup, qubits = generate_device_setup_qubits(
    number_qubits=1,
    shfqc=[
        {
            "serial": "DEV12001",
            "number_of_channels": 6,
            "readout_multiplex": 6,
            "options": None,
        }
    ],
    include_flux_lines=False,
    server_host="localhost",
    setup_name="one_qubit_setup",
)


def compile_and_simulate_experiment(freq_sweep):
    logical_signals = [
        ls
        for lsg in device_setup.logical_signal_groups.values()
        for ls in lsg.logical_signals.values()
    ]
    exp = Experiment(
        signals=[ExperimentSignal(sig.uid.replace("/", "_")) for sig in logical_signals]
    )
    for ls, es in zip(logical_signals, exp.signals):
        exp.map_signal(es, ls)

    pulse = pulse_library.const(uid="pulse", length=100e-9)  # 100 ns per pulse

    # Calibrate drive line with swept software oscillator
    exp.signals["q0_drive"].calibration = SignalCalibration(
        oscillator=Oscillator(
            uid="osc_f1", frequency=freq_sweep, modulation_type=ModulationType.SOFTWARE
        )
    )

    # Sequence: single pulse for each sweep step
    with exp.acquire_loop_rt(1):
        with exp.sweep(uid="sweep", parameter=freq_sweep):
            with exp.section():
                exp.play("q0_drive", pulse)

    # Compile and simulate
    session = Session(device_setup)
    session.connect(do_emulation=True)
    compiled = session.compile(exp, compiler_settings={"OUTPUT_EXTRAS": True})

    plot_simulation(
        compiled,
        start_time=0,
        length=len(freq_sweep.values) * pulse.length,
        signals=["q0_drive"],
    )

device_setup, qubits = generate_device_setup_qubits( number_qubits=1, shfqc=[ { "serial": "DEV12001", "number_of_channels": 6, "readout_multiplex": 6, "options": None, } ], include_flux_lines=False, server_host="localhost", setup_name="one_qubit_setup", ) def compile_and_simulate_experiment(freq_sweep): logical_signals = [ ls for lsg in device_setup.logical_signal_groups.values() for ls in lsg.logical_signals.values() ] exp = Experiment( signals=[ExperimentSignal(sig.uid.replace("/", "_")) for sig in logical_signals] ) for ls, es in zip(logical_signals, exp.signals): exp.map_signal(es, ls) pulse = pulse_library.const(uid="pulse", length=100e-9) # 100 ns per pulse # Calibrate drive line with swept software oscillator exp.signals["q0_drive"].calibration = SignalCalibration( oscillator=Oscillator( uid="osc_f1", frequency=freq_sweep, modulation_type=ModulationType.SOFTWARE ) ) # Sequence: single pulse for each sweep step with exp.acquire_loop_rt(1): with exp.sweep(uid="sweep", parameter=freq_sweep): with exp.section(): exp.play("q0_drive", pulse) # Compile and simulate session = Session(device_setup) session.connect(do_emulation=True) compiled = session.compile(exp, compiler_settings={"OUTPUT_EXTRAS": True}) plot_simulation( compiled, start_time=0, length=len(freq_sweep.values) * pulse.length, signals=["q0_drive"], )

In [ ]:

# Linear frequency sweep parameters
freq_sweep_linear = LinearSweepParameter(
    uid="linear_frequency",
    start=50e6,  # -50 MHz relative to LO (6.95 GHz)
    stop=100e6,  # +50 MHz relative to LO (7.05 GHz)
    count=5,  # 101 frequency points (1 MHz steps)
)

compile_and_simulate_experiment(freq_sweep_linear)

# Linear frequency sweep parameters freq_sweep_linear = LinearSweepParameter( uid="linear_frequency", start=50e6, # -50 MHz relative to LO (6.95 GHz) stop=100e6, # +50 MHz relative to LO (7.05 GHz) count=5, # 101 frequency points (1 MHz steps) ) compile_and_simulate_experiment(freq_sweep_linear)

Example 2: Non-linear Frequency Sweep — Logarithmic¶

In [ ]:

# Create logarithmic frequency sweep
log_frequencies = np.logspace(np.log10(50e6), np.log10(500e6), 11)
freq_sweep_log = SweepParameter(uid="log_frequency", values=log_frequencies)

compile_and_simulate_experiment(freq_sweep_log)

# Create logarithmic frequency sweep log_frequencies = np.logspace(np.log10(50e6), np.log10(500e6), 11) freq_sweep_log = SweepParameter(uid="log_frequency", values=log_frequencies) compile_and_simulate_experiment(freq_sweep_log)

Example 3: Custom Non-Linear Sweep with Dense Sampling¶

This example shows how to create custom frequency patterns with higher resolution around features of interest.

In [ ]:

# Create custom frequency pattern with dense sampling around center frequency
center_freq = 300e6  # 300 MHz

frequencies = np.concatenate(
    [
        np.linspace(100e6, center_freq - 10e6, 3),  # 100, 200, 290 MHz
        np.linspace(
            center_freq - 10e6, center_freq + 10e6, 5
        ),  # 290, 295, 300, 305, 310 MHz
        np.linspace(center_freq + 10e6, 500e6, 3),  # 310, 405, 500 MHz
    ]
)

freq_sweep_dense = SweepParameter(uid="dense_frequency", values=frequencies)

compile_and_simulate_experiment(freq_sweep_dense)

# Summary plots for the frequency list
plt.figure(figsize=(12, 4))

plt.subplot(1, 3, 1)
plt.plot(np.arange(len(frequencies)), frequencies / 1e9, ".-", markersize=4)
plt.xlabel("Step")
plt.ylabel("Frequency (GHz)")
plt.title("Frequency Points")
plt.grid(True)

plt.subplot(1, 3, 2)
freq_diffs = np.diff(frequencies) / 1e6
plt.plot(np.arange(len(freq_diffs)), freq_diffs, ".-", markersize=4)
plt.xlabel("Step")
plt.ylabel("Step Size (MHz)")
plt.title("Frequency Step Sizes")
plt.grid(True)

plt.subplot(1, 3, 3)
plt.hist(frequencies / 1e9, bins=min(20, max(5, len(frequencies) // 3)))
plt.xlabel("Frequency (GHz)")
plt.ylabel("Count")
plt.title("Frequency Distribution")
plt.tight_layout()
plt.show()

# Create custom frequency pattern with dense sampling around center frequency center_freq = 300e6 # 300 MHz frequencies = np.concatenate( [ np.linspace(100e6, center_freq - 10e6, 3), # 100, 200, 290 MHz np.linspace( center_freq - 10e6, center_freq + 10e6, 5 ), # 290, 295, 300, 305, 310 MHz np.linspace(center_freq + 10e6, 500e6, 3), # 310, 405, 500 MHz ] ) freq_sweep_dense = SweepParameter(uid="dense_frequency", values=frequencies) compile_and_simulate_experiment(freq_sweep_dense) # Summary plots for the frequency list plt.figure(figsize=(12, 4)) plt.subplot(1, 3, 1) plt.plot(np.arange(len(frequencies)), frequencies / 1e9, ".-", markersize=4) plt.xlabel("Step") plt.ylabel("Frequency (GHz)") plt.title("Frequency Points") plt.grid(True) plt.subplot(1, 3, 2) freq_diffs = np.diff(frequencies) / 1e6 plt.plot(np.arange(len(freq_diffs)), freq_diffs, ".-", markersize=4) plt.xlabel("Step") plt.ylabel("Step Size (MHz)") plt.title("Frequency Step Sizes") plt.grid(True) plt.subplot(1, 3, 3) plt.hist(frequencies / 1e9, bins=min(20, max(5, len(frequencies) // 3))) plt.xlabel("Frequency (GHz)") plt.ylabel("Count") plt.title("Frequency Distribution") plt.tight_layout() plt.show()

Example 4: Arbitrary Frequency Sequence¶

Sometimes you need completely arbitrary frequency patterns, for example following theoretical predictions or measurement-driven sequences.

In [ ]:

# Create arbitrary frequency sequence (could come from theory, previous measurements, etc.)
arbitrary_freqs = np.array(
    [272e6, 285e6, 279e6, 291e6, 300e6, 276e6, 288e6, 295e6, 282e6, 274e6, 297e6]
)

freq_sweep_arbitrary = SweepParameter(uid="arbitrary_sequence", values=arbitrary_freqs)

compile_and_simulate_experiment(freq_sweep_arbitrary)

# Show the arbitrary sequence pattern
plt.figure(figsize=(10, 6))
plt.subplot(2, 1, 1)
plt.plot(arbitrary_freqs / 1e9, "o-", markersize=8, linewidth=2)
plt.xlabel("Sequence Step")
plt.ylabel("Frequency (GHz)")
plt.title("Arbitrary Frequency Sequence")
plt.grid(True)

plt.subplot(2, 1, 2)
# Show the order in frequency space
sorted_indices = np.argsort(arbitrary_freqs)
plt.plot(arbitrary_freqs / 1e9, np.arange(len(arbitrary_freqs)), "o", markersize=8)
for _i, (freq, step) in enumerate(
    zip(arbitrary_freqs / 1e9, range(len(arbitrary_freqs)))
):
    plt.annotate(f"{step}", (freq, step), xytext=(5, 0), textcoords="offset points")
plt.xlabel("Frequency (GHz)")
plt.ylabel("Sequence Step")
plt.title("Sequence Order vs Frequency")
plt.grid(True)
plt.tight_layout()
plt.show()

# Create arbitrary frequency sequence (could come from theory, previous measurements, etc.) arbitrary_freqs = np.array( [272e6, 285e6, 279e6, 291e6, 300e6, 276e6, 288e6, 295e6, 282e6, 274e6, 297e6] ) freq_sweep_arbitrary = SweepParameter(uid="arbitrary_sequence", values=arbitrary_freqs) compile_and_simulate_experiment(freq_sweep_arbitrary) # Show the arbitrary sequence pattern plt.figure(figsize=(10, 6)) plt.subplot(2, 1, 1) plt.plot(arbitrary_freqs / 1e9, "o-", markersize=8, linewidth=2) plt.xlabel("Sequence Step") plt.ylabel("Frequency (GHz)") plt.title("Arbitrary Frequency Sequence") plt.grid(True) plt.subplot(2, 1, 2) # Show the order in frequency space sorted_indices = np.argsort(arbitrary_freqs) plt.plot(arbitrary_freqs / 1e9, np.arange(len(arbitrary_freqs)), "o", markersize=8) for _i, (freq, step) in enumerate( zip(arbitrary_freqs / 1e9, range(len(arbitrary_freqs))) ): plt.annotate(f"{step}", (freq, step), xytext=(5, 0), textcoords="offset points") plt.xlabel("Frequency (GHz)") plt.ylabel("Sequence Step") plt.title("Sequence Order vs Frequency") plt.grid(True) plt.tight_layout() plt.show()