Quantify dataset - examples#

T1 dataset examples#

The T1 experiment is one of the most common quantum computing experiments. Here we explore how the datasets for such an experiment, for a transmon qubit, can be stored using the Quantify dataset with increasing levels of data detail.

We start with the most simple format that contains only processed (averaged) measurements and finish with a dataset containing the raw digitized signals from the transmon readout during a T1 experiment.

We use a few auxiliary functions to generate, manipulate and plot the data of the examples that follow:

Below you can find the source-code of the most important ones and a few usage examples in order to gain some intuition for the mock data.

Source code for generating mock data Hide code cell content

for func in (mk_iq_shots, mk_trace_time, mk_trace_for_iq_shot):
    display_source_code(func)

def mk_iq_shots(
    num_shots: int = 128,
    sigmas: Union[Tuple[float], NDArray[np.float64]] = (0.1, 0.1),
    centers: Union[Tuple[complex], NDArray[np.complex128]] = (-0.2 + 0.65j, 0.7 + 4j),
    probabilities: Union[Tuple[float], NDArray[np.float64]] = (0.4, 0.6),
    seed: Union[int, None] = 112233,
) -> NDArray:
    """
    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.

    Utility to mock the data coming from qubit readout experiments.
    Clusters are centered around ``centers`` and data points are distributed between
    them according to ``probabilities``.

    .. seealso:: :ref:`howto-utilities-examples-ssro`

    Parameters
    ----------
    num_shots
        The number of shot to generate.
    sigma
        The sigma of the Gaussian distribution used for both real and imaginary parts.
    centers
        The center of each cluster on the imaginary plane.
    probabilities
        The probabilities of each cluster being randomly selected for each shot.
    seed
        Random number generator seed passed to ``numpy.random.default_rng``.
    """
    if not len(sigmas) == len(centers) == len(probabilities):
        raise ValueError(
            f"Incorrect input. sigmas={sigmas}, centers={centers} and "
            f"probabilities={probabilities} must have the same length."
        )

    rng = np.random.default_rng(seed=seed)

    cluster_indices = tuple(range(len(centers)))
    choices = rng.choice(a=cluster_indices, size=num_shots, p=probabilities)

    shots = []
    for idx in cluster_indices:
        num_shots_this_cluster = np.sum(choices == idx)
        i_data = rng.normal(
            loc=centers[idx].real,
            scale=sigmas[idx],
            size=num_shots_this_cluster,
        )
        q_data = rng.normal(
            loc=centers[idx].imag,
            scale=sigmas[idx],
            size=num_shots_this_cluster,
        )
        shots.append(i_data + 1j * q_data)
    return np.concatenate(shots)

def mk_trace_time(sampling_rate: float = 1e9, duration: float = 0.3e-6) -> NDArray:
    """
    Generates a :obj:`~numpy.arange` in which the entries correspond to time instants
    up to ``duration`` seconds sampled according to ``sampling_rate`` in Hz.

    See :func:`~.mk_trace_for_iq_shot` for an usage example.

    Parameters
    ----------
    sampling_rate
        The sampling rate in Hz.
    duration
        Total duration in seconds.

    Returns
    -------
    :
        An array with the time instants.
    """
    trace_length = sampling_rate * duration
    return np.arange(0, trace_length, 1) / sampling_rate

def mk_trace_for_iq_shot(
    iq_point: complex,
    time_values: Optional[NDArray] = None,
    intermediate_freq: float = 50e6,
) -> NDArray:
    """
    Generates mock "traces" that a physical instrument would digitize for the readout of
    a transmon qubit when using a down-converting IQ mixer.

    .. seealso:: :ref:`howto-utilities-examples-trace`

    Parameters
    ----------
    iq_point
        A complex number representing a point on the IQ-plane.
    time_values
        The time instants at which the mock intermediate-frequency signal is sampled.
    intermediate_freq
        The intermediate frequency used in the down-conversion scheme.

    Returns
    -------
    :
        An array of complex numbers.
    """  # pylint: disable=line-too-long
    if time_values is None:
        time_values = mk_trace_time()
    return iq_point * np.exp(2.0j * np.pi * intermediate_freq * time_values)

ground = -0.2 + 0.65j
excited = 0.7 - 0.4j
centers = ground, excited
sigmas = [0.1] * 2

shots = mk_iq_shots(
    num_shots=256,
    sigmas=sigmas,
    centers=centers,
    probabilities=[0.4, 1 - 0.4],
)

plt.hexbin(shots.real, shots.imag)
plt.xlabel("I")
plt.ylabel("Q")
_ = plot_complex_points(centers, ax=plt.gca())

../../../_images/e732ebcb97485dee67a70dd8cdeda600de53994e50bd0b2fd7d4801f0ac7fed8.png

time = mk_trace_time()
trace = mk_trace_for_iq_shot(shots[0])

fig, ax = plt.subplots(1, 1, figsize=(12, 12 / 1.61 / 2))
ax.plot(time * 1e6, trace.imag, ".-", label="I-quadrature")
ax.plot(time * 1e6, trace.real, ".-", label="Q-quadrature")
ax.set_xlabel("Time [µs]")
ax.set_ylabel("Amplitude [V]")
_ = ax.legend()

../../../_images/d21dc7ec26674f55b237a548bc1f012ee7ed9bf8207b50d8c8d7644382d02d93.png

First, we define a few parameters of our mock qubit and mock data acquisition.

# parameters of our qubit model
tau = 30e-6
ground = -0.2 + 0.65j  # ground state on the IQ-plane
excited = 0.7 - 0.4j  # excited state on the IQ-plane
centers = ground, excited
sigmas = [0.1] * 2  # sigma, NB in general not the same for both state

# mock of data acquisition configuration
# NB usually at least 1000+ shots are taken, here we use less for faster code execution
num_shots = 256
# time delays between exciting the qubit and measuring its state
t1_times = np.linspace(0, 120e-6, 30)

# NB this are the ideal probabilities from repeating the measurement many times for a
# qubit with a lifetime given by tau
probabilities = exp_decay_func(t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1)

# Ideal experiment result
plt.ylabel("|1> probability")
plt.suptitle("Typical processed data of a T1 experiment")
plt.plot(t1_times * 1e6, probabilities, ".-")
_ = plt.xlabel("Time [µs]")

../../../_images/384d8caf1766b88206262b8aae3470029a991910aca9d49a6cc285fdc0bb6fdf.png

# convenience dict with the mock parameters
mock_conf = dict(
    num_shots=num_shots,
    centers=centers,
    sigmas=sigmas,
    t1_times=t1_times,
    probabilities=probabilities,
)

Data rotation and normalization utilities#

The normalization of the calibration points can be achieved as follows. Several of the single-qubit time-domain analyses provided use this under the hood. The result is that most of the information will now be contained within the same quadrature.

rotated_and_normalized = rotate_to_calibrated_axis(
    dataset_gridded.q0_iq_av.values, *dataset_gridded.q0_iq_av_cal.values
)
rotated_and_normalized_da = xr.DataArray(dataset_gridded.q0_iq_av)
rotated_and_normalized_da.values = rotated_and_normalized
rotated_and_normalized_da.attrs["long_name"] = "|1> Population"
rotated_and_normalized_da.attrs["units"] = ""
_ = plot_xr_complex(rotated_and_normalized_da)