corrections#

Pulse and acquisition corrections for hardware compilation.

Module Contents#

Functions#

determine_relative_latency_corrections(→ dict[str, float])

Generates the latency configuration dict for all port-clock combinations that are present in

distortion_correct_pulse(...)

Sample pulse and apply filter function to the sample to distortion correct it.

_is_distortion_correctable(→ bool)

Checks whether distortion corrections can be applied to the given operation.

apply_software_distortion_corrections(…)

Apply distortion corrections to operations in the schedule.

Attributes#

logger

logger[source]#
determine_relative_latency_corrections(hardware_cfg: quantify_scheduler.backends.types.common.HardwareCompilationConfig | dict[str, Any], schedule: quantify_scheduler.schedules.schedule.Schedule | None = None) → dict[str, float][source]#

Generates the latency configuration dict for all port-clock combinations that are present in the schedule (or in the hardware config, if an old-style zhinst config is passed). This is done by first setting unspecified latency corrections to zero, and then subtracting the minimum latency from all latency corrections.

distortion_correct_pulse(pulse_data: dict[str, Any], distortion_correction: quantify_scheduler.backends.types.common.SoftwareDistortionCorrection) → quantify_scheduler.operations.pulse_library.NumericalPulse[source]#

Sample pulse and apply filter function to the sample to distortion correct it.

Parameters:

  • pulse_data – Definition of the pulse.

  • distortion_correction – The distortion_correction configuration for this pulse.

Returns:

The sampled, distortion corrected pulse wrapped in a NumericalPulse.

_is_distortion_correctable(operation: quantify_scheduler.operations.operation.Operation) → bool[source]#

Checks whether distortion corrections can be applied to the given operation.

apply_software_distortion_corrections(operation: quantify_scheduler.operations.operation.Operation, distortion_corrections: dict) → quantify_scheduler.operations.operation.Operation | None[source]#
apply_software_distortion_corrections(operation: quantify_scheduler.schedules.schedule.Schedule, distortion_corrections: dict) → None

Apply distortion corrections to operations in the schedule.

Defined via the hardware configuration file, example:

"distortion_corrections": {
    "q0:fl-cl0.baseband": {
        "filter_func": "scipy.signal.lfilter",
        "input_var_name": "x",
        "kwargs": {
            "b": [0.0, 0.5, 1.0],
            "a": [1]
        },
        "clipping_values": [-2.5, 2.5]
    }
}

Clipping values are the boundaries to which the corrected pulses will be clipped, upon exceeding, these are optional to supply.

For pulses in need of correcting (indicated by their port-clock combination) we are only replacing the dict in "pulse_info" associated to that specific pulse. This means that we can have a combination of corrected (i.e., pre-sampled) and uncorrected pulses in the same operation.

Note that we are not updating the "operation_id" key, used to reference the operation from schedulables.

Parameters:

  • operation – The operation that contains operations that are to be distortion corrected. Note, this function updates the operation.

  • distortion_corrections – The distortion_corrections configuration of the setup.

Returns:

The new operation with distortion corrected operations, if it needs to be replaced. If it doesn’t need to be replaced in the schedule or control flow, it returns None.

Warns:

RuntimeWarning – If distortion correction can not be applied to the type of Operation in the schedule.

Raises:

  • KeyError – when elements are missing in distortion correction config for a port-clock combination.

  • KeyError – when clipping values are supplied but not two values exactly, min and max.