qblox_scheduler

Package Contents

Classes

DeviceCompilationConfig	Information required to compile a schedule to the quantum-device layer.
OperationCompilationConfig	Information required to compile an individual operation to the quantum-device layer.
DistortionCorrectionLatencyEnum	Settings related to distortion corrections.
Connectivity	Connectivity between ports on the quantum device and control hardware inputs/outputs.
HardwareDistortionCorrection	Hardware distortion correction settings for a port-clock combination.
MixerCorrections	Mixer corrections for a port-clock combination.
ComplexInputGain	Input gain settings for a complex input connected to a port-clock combination.
DigitizationThresholds	The settings that determine when an analog voltage is counted as a pulse.
NetZeroPulseParameters	The settings to control the net-zero compensation pulse.
ExpOvershootFilter	An individual real time filter on Qblox hardware.
FIRFilter	An individual real time filter on Qblox hardware.
IHPFilter	An individual real time filter on Qblox hardware.
OutputDirectoryManager	Manages output directory paths for Qblox Scheduler data storage.
ExperimentDataContainer	Class which represents all data related to an experiment. This allows the user to
DeviceElement	Create a device element for managing parameters.
Edge	Create an Edge.
QuantumDevice	The QuantumDevice directly represents the device under test (DUT).
TriggerCondition	Comparison condition for the thresholded trigger count acquisition.
MockLocalOscillator	A class representing a dummy Local Oscillator.
Acquisition	An operation representing data acquisition at the quantum-device abstraction layer.
DualThresholdedTriggerCount	Thresholded trigger count protocol that uses two thresholds.
IQIntegration	Weighted integration acquisition protocol returning integrated I and Q as one complex number.
IQIntegrationMixed	Subclass of IQIntegration returning a real number, combining weighted I and Q.
ThresholdedIQIntegration	Acquisition protocol allowing to control rotation and threshold.
ThresholdedIQIntegrationMixed	Subclass of IQIntegrationMixed but Thresholded.
ThresholdedTriggerCount	Thresholded trigger counting acquisition protocol returning the comparison result with a
Timetag	Acquire a single timetag per acquisition index.
TimetagTrace	The TimetagTrace acquisition protocol records timetags within an acquisition window.
Trace	The Trace acquisition protocol measures a signal s(t).
TriggerCount	Trigger counting acquisition protocol returning an integer.
TriggerCountDistribution	Trigger counting distribution acquisition protocol returning an integer.
ConditionalReset	Reset a qubit to the \(|0\rangle\) state.
ConditionalOperation	Conditional over another operation.
LoopStrategy	Strategy to use for implementing loops.
DType	Data type of a variable or expression.
CNOT	Conditional-NOT gate, a common entangling gate.
CZ	Conditional-phase gate, a common entangling gate.
X90	A single qubit rotation of 90 degrees around the X-axis.
Y90	A single qubit rotation of 90 degrees around the Y-axis.
Z90	A single qubit rotation of 90 degrees around the Z-axis.
H	A single qubit Hadamard gate.
Measure	A projective measurement in the Z-basis.
Reset	Reset a qubit to the \(|0\rangle\) state.
Rxy	A single qubit rotation around an axis in the equator of the Bloch sphere.
Rz	A single qubit rotation about the Z-axis of the Bloch sphere.
S	A single qubit rotation of 90 degrees around the Z-axis.
SDagger	A single qubit rotation of -90 degrees around the Z-axis.
T	A single qubit rotation of 45 degrees around the Z-axis.
TDagger	A single qubit rotation of -45 degrees around the Z-axis.
X	A single qubit rotation of 180 degrees around the X-axis.
Y	A single qubit rotation of 180 degrees around the Y-axis.
Z	A single qubit rotation of 180 degrees around the Z-axis.
InlineQ1ASM	Initialize an InlineQ1ASM operation.
LatchReset	Operation that resets the feedback trigger addresses from the hardware.
SimpleNumericalPulse	Wrapper on top of NumericalPulse to provide a simple interface for creating a pulse
Operation	A representation of quantum circuit operations.
PulseCompensation	Apply pulse compensation to an operation or schedule.
ChirpPulse	A linear chirp signal. A sinusoidal signal that ramps up in frequency.
DRAGPulse	A Gaussian pulse with a derivative component added to the out-of-phase channel.
GaussPulse	The GaussPulse Operation is a real-valued pulse with the specified
IdlePulse	The IdlePulse Operation is a placeholder for a specified duration of time.
MarkerPulse	Digital pulse that is HIGH for the specified duration.
NumericalPulse	A pulse where the shape is determined by specifying an array of (complex) points.
RampPulse	RampPulse Operation is a pulse that ramps from zero to a set amplitude over its duration.
ResetClockPhase	An operation that resets the phase of the NCO clock.
SetClockFrequency	Operation that sets updates the frequency of a clock.
ShiftClockPhase	Operation that shifts the phase of a clock by a specified amount.
SkewedHermitePulse	Hermite pulse intended for single qubit gates in diamond based systems.
SoftSquarePulse	A real valued square pulse convolved with a Hann window for smoothing.
SquarePulse	A real-valued pulse with the specified amplitude during the pulse.
StaircasePulse	A real valued staircase pulse, which reaches it's final amplitude in discrete steps.
SuddenNetZeroPulse	A pulse that can be used to implement a conditional phase gate in transmon device elements.
Timestamp	Operation that marks a time reference for timetags.
VoltageOffset	Operation that represents setting a constant offset to the output voltage.
HardwareAgent	Hardware definition of qblox backend.
BasebandClockResource	Global identity for a virtual baseband clock.
ClockResource	The ClockResource corresponds to a physical clock used to modulate pulses.
DigitalClockResource	Global identity for a virtual digital clock.
Schedule	High-level hybrid schedule.
CompiledSchedule	A schedule that contains compiled instructions ready for execution using the InstrumentCoordinator.
Loop	Experiment segment that loops other segments over some values.
Callback	Experiment segment that calls a generic, user-defined callback.
SetHardwareDescriptionField	Experiment segment that sets a hardware description parameter for a given instrument.
SetHardwareOption	Experiment segment that sets a hardware option for a given port/clock.
SetParameter	Experiment segment that sets a QCoDeS parameter, or device element parameter.
Numbers	Dict used to emulate the behaviour of the Numbers qcodes validator.
SchedulerSubmodule	Compatibility class emulating the behaviour of InstrumentModule/InstrumentChannel.

Functions

arange(…)	Linear range of values to loop over, specified with a start value, an exclusive stop value and a
linspace(→ LinearDomain)	Linear range of values to loop over, specified with a start value, an inclusive stop value and
Parameter(→ Any)	Wrapper function around :func:~pydantic.Field that tries to emulate qcodes parameters

Attributes

__version__
InputAttenuation	Input attenuation setting for a port-clock combination.
OutputAttenuation	Output attenuation setting for a port-clock combination.
RealInputGain	Input gain settings for a real input connected to a port-clock combination.

class DeviceCompilationConfig(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

Information required to compile a schedule to the quantum-device layer.

During compilation this information is needed to convert a schedule defined on a quantum-circuit layer to a schedule defined on a quantum-device layer.

Examples

The DeviceCompilationConfig is structured such that it should allow the specification of the circuit-to-device compilation for many different qubit platforms. Here we show a basic configuration for a single-element device, specifying how the reset and Rxy gates are compiled to device-level pulses. In this example, the DeviceCompilationConfig is created by parsing a dictionary containing the relevant information.

Important

Although it is possible to manually create a configuration using dictionaries, this is not recommended. The QuantumDevice is responsible for managing and generating configuration files. Ready-made device elements for specific qubit platforms (e.g. transmons, spin qubits, or NV centers) are available in qblox-applications.

import pprint
from qblox_scheduler.backends.graph_compilation import (
    DeviceCompilationConfig
)

example_device_cfg = {
    "clocks": {
        "q0.01": 6020000000.0,
    },
    "elements": {
        "q0": {
            "reset": {
                "factory_func": (
                    "qblox_scheduler.operations.pulse_library.IdlePulse"
                ),
                "factory_kwargs": {"duration": 0.0002},
            },
            "Rxy": {
                "factory_func": (
                    "qblox_scheduler.operations."
                    "pulse_factories.rxy_drag_pulse"
                ),
                "gate_info_factory_kwargs": ["theta", "phi"],
                "factory_kwargs": {
                    "amp180": 0.32,
                    "beta": 1.125e-9,
                    "port": "q0:mw",
                    "clock": "q0.01",
                    "duration": 2e-08,
                },
            },
        },
    },
    "edges": {},
}

pprint.pprint(example_device_cfg)

{'clocks': {'q0.01': 6020000000.0},
 'edges': {},
 'elements': {'q0': {'Rxy': {'factory_func': 'qblox_scheduler.operations.pulse_factories.rxy_drag_pulse',
                             'factory_kwargs': {'amp180': 0.32,
                                                'beta': 1.125e-09,
                                                'clock': 'q0.01',
                                                'duration': 2e-08,
                                                'port': 'q0:mw'},
                             'gate_info_factory_kwargs': ['theta', 'phi']},
                     'reset': {'factory_func': 'qblox_scheduler.operations.pulse_library.IdlePulse',
                               'factory_kwargs': {'duration': 0.0002}}}}}

The dictionary can be parsed using the model_validate method.

device_cfg = DeviceCompilationConfig.model_validate(example_device_cfg)
device_cfg

DeviceCompilationConfig(clocks={'q0.01': 6020000000.0}, elements={'q0': {'reset': OperationCompilationConfig(factory_func=<class 'qblox_scheduler.operations.pulse_library.IdlePulse'>, factory_kwargs={'duration': 0.0002}, gate_info_factory_kwargs=None), 'Rxy': OperationCompilationConfig(factory_func=<function rxy_drag_pulse at 0x7fe74be93530>, factory_kwargs={'amp180': 0.32, 'beta': 1.125e-09, 'port': 'q0:mw', 'clock': 'q0.01', 'duration': 2e-08}, gate_info_factory_kwargs=['theta', 'phi'])}}, edges={}, scheduling_strategy=<SchedulingStrategy.ASAP: 'asap'>, compilation_passes=[SimpleNodeConfig(name='unroll_loops', compilation_func=<function _unroll_loops at 0x7fe74b031fe0>), SimpleNodeConfig(name='circuit_to_device', compilation_func=<function compile_circuit_to_device_with_config_validation at 0x7fe74b352610>), SimpleNodeConfig(name='schedule_repetitions_to_loop', compilation_func=<function schedule_repetitions_to_loop at 0x7fe74b032400>), SimpleNodeConfig(name='set_pulse_and_acquisition_clock', compilation_func=<function set_pulse_and_acquisition_clock at 0x7fe74b352ae0>), SimpleNodeConfig(name='process_compensation_pulses', compilation_func=<function process_compensation_pulses at 0x7fe74af8afb0>), SimpleNodeConfig(name='determine_absolute_timing', compilation_func=<function _determine_absolute_timing at 0x7fe74b030ca0>), SimpleNodeConfig(name='normalize_absolute_timing', compilation_func=<function _normalize_absolute_timing at 0x7fe74b031bc0>), SimpleNodeConfig(name='scale_amplitude_variables', compilation_func=<function scale_amplitude_variables at 0x7fe74b032c40>), SimpleNodeConfig(name='scale_phase_domains', compilation_func=<function scale_phase_domains at 0x7fe74b032da0>)])

clocks: dict[str, float]

A dictionary specifying the clock frequencies available on the device e.g., {"q0.01": 6.123e9}.

elements: dict[str, dict[str, OperationCompilationConfig]]

A dictionary specifying the elements on the device, what operations can be applied to them and how to compile these.

edges: dict[str, dict[str, OperationCompilationConfig]]

A dictionary specifying the edges, links between elements on the device to which operations can be applied, and the operations that can be applied to them and how to compile these.

scheduling_strategy: qblox_scheduler.enums.SchedulingStrategy

The scheduling strategy used when determining the absolute timing of each operation of the schedule.

compilation_passes: list[SimpleNodeConfig]

The list of compilation nodes that should be called in succession to compile a schedule to the quantum-device layer.

class OperationCompilationConfig(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

Information required to compile an individual operation to the quantum-device layer.

During compilation this information is needed to convert an operation defined on a quantum-circuit layer to an operation defined on a quantum-device layer.

factory_func: collections.abc.Callable[..., qblox_scheduler.operations.operation.Operation | qblox_scheduler.schedules.schedule.TimeableSchedule] | None

A callable designating a factory function used to create the representation of the operation at the quantum-device level.

factory_kwargs: dict[str, Any]

A dictionary containing the keyword arguments and corresponding values to use when creating the operation by evaluating the factory function.

gate_info_factory_kwargs: list[str] | None = None

A list of keyword arguments of the factory function for which the value must be retrieved from the gate_info of the operation.

_serialize_factory_func(v: object) → str | None

classmethod _import_factory_func_if_str(fun: str | collections.abc.Callable[..., qblox_scheduler.operations.operation.Operation]) → collections.abc.Callable[..., qblox_scheduler.operations.operation.Operation]

class DistortionCorrectionLatencyEnum

Bases: int, enum.Enum

Settings related to distortion corrections.

NO_DELAY_COMP = 0

Setting for no distortion correction delay compensation

IHP = 1

Setting for delay compensation equal to the IHP filter.

EXP0 = 2

Setting for delay compensation equal to exponential overshoot or undershoot correction

EXP1 = 4

Setting for delay compensation equal to exponential overshoot or undershoot correction

EXP2 = 8

Setting for delay compensation equal to exponential overshoot or undershoot correction

EXP3 = 16

Setting for delay compensation equal to exponential overshoot or undershoot correction

FIR = 32

Setting for delay compensation equal to FIR filter

class Connectivity(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

Connectivity between ports on the quantum device and control hardware inputs/outputs.

The connectivity graph can be parsed from a list of edges, which are given by a set of two strings that each correspond to an input/output on an instrument or a port on the quantum device.

Note

To specify connections between more than one pair of ports at once, one can also specify a list of ports within the edge input (see example below).

The connectivity graph can be drawn using draw(), which groups the nodes according to the instrument name (specified by the string before the first "." in the node name; the name is omitted for the quantum device).

Example

from qblox_scheduler import Connectivity

connectivity_dict = {
    "graph": [
        ("awg0.channel_0", "q0:mw"),
        ("awg0.channel_1", "q1:mw"),
        ("rom0.channel_0", ["q0:res", "q1:res"]),
    ]
}

connectivity = Connectivity.model_validate(connectivity_dict)
connectivity.draw()

<Axes: >

graph: qblox_scheduler.structure.types.Graph

The connectivity graph consisting of i/o ports (nodes) on the quantum device and on the control hardware, and their connections (edges).

classmethod _unroll_lists_of_ports_in_edges_input(graph) → list[tuple[Any, Any]]

serialize_graph(graph: qblox_scheduler.structure.types.Graph) → list[tuple[Any, Any]]

Serialize the graph as a list of edges.

draw(ax: matplotlib.axes.Axes | None = None, figsize: tuple[float, float] = (20, 10), **options) → matplotlib.axes.Axes

Draw the connectivity graph using matplotlib.

The nodes are positioned using a multipartite layout, where the nodes are grouped by instrument (identified by the first part of the node name).

Parameters:

• ax – Matplotlib axis to plot the figure on.

• figsize – Optional figure size, defaults to something slightly larger that fits the size of the nodes.

• options – optional keyword arguments that are passed to networkx.draw_networkx.

Returns:

: Matplotlib axis on which the figure is plotted.

class HardwareDistortionCorrection(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

Hardware distortion correction settings for a port-clock combination.

exp0_coeffs: ExpCoeffs | None = None

Coefficients of the exponential overshoot/undershoot correction 1.

exp1_coeffs: ExpCoeffs | None = None

Coefficients of the exponential overshoot/undershoot correction 2.

exp2_coeffs: ExpCoeffs | None = None

Coefficients of the exponential overshoot/undershoot correction 3.

exp3_coeffs: ExpCoeffs | None = None

Coefficients of the exponential overshoot/undershoot correction 4.

fir_coeffs: list[float] | None = None

Coefficients for the FIR filter.

ihp_coeffs: IHPCoeffs | None = None

Coefficients for the IHP filter.

classmethod fir_coeffs_sum_to_1(value: list[float] | None) → list[float] | None

Validate whether the FIR coefficients sum up to 1.

class MixerCorrections(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

Mixer corrections for a port-clock combination.

Example

from qblox_scheduler import MixerCorrections

mixer_corrections = {
    "q0:mw-q0.01": MixerCorrections(
        dc_offset_i = -0.0542,
        dc_offset_q = -0.0328,
        amp_ratio = 0.95,
        phase_error= 0.07,
    )
}

dc_offset_i: float | None = None

The DC offset on the I channel used for this port-clock combination.

dc_offset_q: float | None = None

The DC offset on the Q channel used for this port-clock combination.

amp_ratio: float | None

The mixer gain ratio used for this port-clock combination.

phase_error: float | None

The mixer phase error used for this port-clock combination.

auto_lo_cal: qblox_scheduler.backends.qblox.enums.LoCalEnum

Setting that controls whether the mixer is calibrated upon changing the LO and/or intermodulation frequency.

auto_sideband_cal: qblox_scheduler.backends.qblox.enums.SidebandCalEnum

Setting that controls whether the mixer is calibrated upon changing the intermodulation frequency.

classmethod warn_if_mixed_auto_and_manual_calibration(data: dict[str, Any]) → dict[str, Any]

Warn if there is mixed usage of automatic mixer calibration (the auto_* settings) and manual mixer correction settings.

class ComplexInputGain(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

Input gain settings for a complex input connected to a port-clock combination.

This gain value will be set on the QRM input ports that are connected to this port-clock combination.

Example

hardware_compilation_config.hardware_options.input_gain = {
    "q0:res-q0.ro": ComplexInputGain(
        gain_I=2,
        gain_Q=3
    ),
}

gain_I: int

Gain setting on the input receiving the I-component data for this port-clock combination.

gain_Q: int

Gain setting on the input receiving the Q-component data for this port-clock combination.

class DigitizationThresholds(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

The settings that determine when an analog voltage is counted as a pulse.

analog_threshold: float | None = None

For QTM modules only, this is the voltage threshold above which an input signal is registered as high.

InputAttenuation

Input attenuation setting for a port-clock combination.

This attenuation value will be set on each control-hardware output port that is connected to this port-clock combination.

Example

hardware_compilation_config.hardware_options.input_att = {
    "q0:res-q0.ro": InputAttenuation(10),
}

class NetZeroPulseParameters(/, **data: Any)

Bases: qblox_scheduler.structure.model.DataStructure

The settings to control the net-zero compensation pulse.

max_amplitude: float

The maximum amplitude of the net-zero compensation pulse, in V/V_max.

slew_rate: float

The slew rate of the net-zero compensation pulse, in V/V_max/s.

OutputAttenuation

Output attenuation setting for a port-clock combination.

This attenuation value will be set on each control-hardware output port that is connected to this port-clock combination.

Example

hardware_compilation_config.hardware_options.output_att = {
    "q0:res-q0.ro": OutputAttenuation(10),
}

RealInputGain

Input gain settings for a real input connected to a port-clock combination.

This gain value will be set on the QRM input ports that are connected to this port-clock combination.

Example

hardware_compilation_config.hardware_options.input_gain = {
    "q0:res-q0.ro": RealInputGain(2),
}

class ExpOvershootFilter

Bases: RealTimeFilterBase

An individual real time filter on Qblox hardware.

coeffs: qblox_scheduler.backends.types.common.ExpCoeffs | None = None

Coefficient(s) of the filter. Can be None if there is no filter or if it is inactive.

class FIRFilter

Bases: RealTimeFilterBase

An individual real time filter on Qblox hardware.

coeffs: list[float] | None = None

Coefficient(s) of the filter. Can be None if there is no filter or if it is inactive.

class IHPFilter

Bases: RealTimeFilterBase

An individual real time filter on Qblox hardware.

coeffs: qblox_scheduler.backends.types.common.IHPCoeffs | None = None

Coefficient(s) of the filter. Can be None if there is no filter or if it is inactive.

class OutputDirectoryManager

Manages output directory paths for Qblox Scheduler data storage.

The class maintains a single instance throughout the application lifecycle, ensuring consistent directory management.

_datadir

The current data directory path. Private attribute managed through setter and getter methods.

Type:

str or Path

DATADIR: ClassVar[pathlib.Path]

classmethod set_datadir(datadir: pathlib.Path | str | None = None) → None

Sets the data directory.

Parameters:

datadir (pathlib.Path or str or None) – Path of the data directory. If set to None, resets the datadir to the default datadir (<top_level>/data).

classmethod get_datadir() → pathlib.Path

Returns the current data directory.

Returns:

: The current data directory.

class ExperimentDataContainer(tuid: str, name: str)

Class which represents all data related to an experiment. This allows the user to run experiments and store data. The class serves as an initial interface and uses the directory paths set by OutputDirectoryManager.

DATASET_NAME: ClassVar[str] = 'dataset.hdf5'

SNAPSHOT_FILENAME: ClassVar[str] = 'snapshot.json'

_TUID_LENGTH: ClassVar[int] = 26

tuid

day_folder

data_folder

property experiment_name: str

The name of the experiment.

classmethod load_dataset(tuid: qblox_scheduler.quantify_utils.TUID, name: str = DATASET_NAME) → xarray.Dataset

Loads a dataset specified by a tuid.

Parameters:

• tuid – A TUID string. It is also possible to specify only the first part of a tuid.

• name – Name of the dataset.

Returns:

: The dataset.

classmethod load_dataset_from_path(path: pathlib.Path | str) → xarray.Dataset

Loads a Dataset with a specific engine preference.

Before returning the dataset AdapterH5NetCDF.recover() is applied.

This function tries to load the dataset until success with the following engine preference:

• "h5netcdf"

• "netcdf4"

• No engine specified (load_dataset() default)

Parameters:

path – Path to the dataset.

Returns:

: The loaded dataset.

write_dataset(dataset: xarray.Dataset) → None

Writes the quantify dataset to the directory specified by ~.data_folder.

Parameters:

dataset – The dataset to be written to the directory

save_snapshot(snapshot: dict[str, Any] | None = None, compression: Literal['bz2', 'gzip', 'lzma'] | None = None) → None

Writes the snapshot to disk as specified by ~.data_folder.

Parameters:

• snapshot – The snapshot to be written to the directory

• compression – The compression type to use. Can be one of ‘gzip’, ‘bz2’, ‘lzma’. Defaults to None, which means no compression.

classmethod get_latest_tuid(contains: str = '') → qblox_scheduler.quantify_utils.TUID

Returns the most recent tuid.

Tip

This function is similar to get_tuids_containing() but is preferred if one is only interested in the most recent TUID for performance reasons.

Parameters:

contains – An optional string contained in the experiment name.

Returns:

: The latest TUID.

Raises:

FileNotFoundError – No data found.

classmethod get_tuids_containing(contains: str = '', t_start: datetime.datetime | str | None = None, t_stop: datetime.datetime | str | None = None, max_results: int = sys.maxsize, reverse: bool = False) → list[qblox_scheduler.quantify_utils.TUID]

Returns a list of tuids containing a specific label.

Tip

If one is only interested in the most recent TUID, get_latest_tuid() is preferred for performance reasons.

Parameters:

• contains – A string contained in the experiment name.

• t_start – datetime to search from, inclusive. If a string is specified, it will be converted to a datetime object using parse. If no value is specified, will use the year 1 as a reference t_start.

• t_stop – datetime to search until, exclusive. If a string is specified, it will be converted to a datetime object using parse. If no value is specified, will use the current time as a reference t_stop.

• max_results – Maximum number of results to return. Defaults to unlimited.

• reverse – If False, sorts tuids chronologically, if True sorts by most recent.

Returns:

list A list of TUID: objects.

Raises:

FileNotFoundError – No data found.

classmethod locate_experiment_container(tuid: str) → pathlib.Path

Returns the experiment container for the given tuid.

class DeviceElement(/, name, **data: Any)

Bases: abc.ABC, qblox_scheduler.structure.model.SchedulerBaseModel

Create a device element for managing parameters.

The DeviceElement is responsible for compiling operations applied to that specific device element from the composite to the quantum-device layer.

element_type: str

name: str

classmethod include_submodule_names(data: Any) → Any

Fill in the name attribute of DeviceElement submodules when missing (used for YAML deserialization, they are omitted at serialization).

classmethod _drop_reference_magnitude(data: Any) → Any

classmethod dispatch_concrete_model(data: Any, handler: pydantic.ModelWrapValidatorHandler[Self]) → Self

When deserializing a dict representation of a concrete DeviceElement, infer the matching class by looking its element_type into the model registry and return a validated instance of the concrete device element.

abstractmethod generate_device_config() → qblox_scheduler.backends.graph_compilation.DeviceCompilationConfig

Generate the device configuration.

class Edge(parent_element: qblox_scheduler.device_under_test.device_element.DeviceElement | str | None = None, child_element: qblox_scheduler.device_under_test.device_element.DeviceElement | str | None = None, **data: Any)

Bases: abc.ABC, qblox_scheduler.structure.model.SchedulerBaseModel

Create an Edge.

This class encapsulates the connection information between DeviceElements in the QuantumDevice. It provides an interface for the QuantumDevice to generate the edge information for use in the device compilation step. See qblox_scheduler.device_under_test.composite_square_edge for an example edge implementation.

edge_type: str

parent_element_name: str

child_element_name: str

_parent_device_element: qblox_scheduler.device_under_test.device_element.DeviceElement | None

_child_device_element: qblox_scheduler.device_under_test.device_element.DeviceElement | None

classmethod include_submodule_names(data: Any) → Any

Fill in the name attribute of Edge submodules when missing (used for YAML deserialization, they are omitted at serialization).

classmethod dispatch_concrete_model(data: Any, handler: pydantic.ModelWrapValidatorHandler[Self]) → Self

When deserializing a dict representation of a concrete Edge, infer the matching class by looking its edge_type into the model registry and return a validated instance of the concrete edge.

property parent_element: qblox_scheduler.device_under_test.device_element.DeviceElement | None

Getter for the internal parent device element.

property child_element: qblox_scheduler.device_under_test.device_element.DeviceElement | None

Getter for the internal child device element.

property submodules: dict[str, Any]

Mapping of submodules of this edge.

static _get_element_name(element: qblox_scheduler.device_under_test.device_element.DeviceElement | dict | str | None) → str

Get the name of an element represented as a DeviceElement instance, dictionary or string.

abstractmethod generate_edge_config() → dict[str, dict[str, qblox_scheduler.backends.graph_compilation.OperationCompilationConfig]]

Generate the device configuration for an edge.

This method is intended to be used when this object is part of a device object containing multiple elements.

class QuantumDevice(/, name, **data: Any)

Bases: qblox_scheduler.structure.model.SchedulerBaseModel

The QuantumDevice directly represents the device under test (DUT).

This contains a description of the connectivity to the control hardware as well as parameters specifying quantities like cross talk, attenuation and calibrated cable-delays. The QuantumDevice also contains references to individual DeviceElements, representations of elements on a device (e.g, a transmon qubit) containing the (calibrated) control-pulse parameters.

This object can be used to generate configuration files for the compilation step from the gate-level to the pulse level description. These configuration files should be compatible with the compile() function.

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

elements: dict[str, ConcreteDeviceElement]

edges: dict[str, ConcreteEdge]

instr_instrument_coordinator: qblox_scheduler.instrument_coordinator.InstrumentCoordinator | None

cfg_sched_repetitions: pydantic.PositiveInt

keep_original_schedule: bool

hardware_config: qblox_scheduler.backends.types.common.HardwareCompilationConfig | dict | None = None

scheduling_strategy: qblox_scheduler.enums.SchedulingStrategy

classmethod validate_instrument_coordinator(value: str | qblox_scheduler.instrument_coordinator.InstrumentCoordinator | None) → qcodes.instrument.Instrument | None

Load InstrumentCoordinator instance from its name.

Pydantic doesn’t know how to handle a QCoDeS instrument; thus, we have to allow arbitrary types and manually fetch them with find_or_create_instrument.

classmethod validate_scheduling_strategy(value: str | qblox_scheduler.enums.SchedulingStrategy) → qblox_scheduler.enums.SchedulingStrategy

Force scheduling_strategy into its proper enum value.

classmethod validate_elements_and_edges(data: Any, handler: pydantic.ModelWrapValidatorHandler[Self]) → Self

Add elements and edges to the model by calling add_element and add_edge respectively to force our consistency checks.

generate_compilation_config() → qblox_scheduler.backends.graph_compilation.SerialCompilationConfig

Generate a config for use with a ScheduleCompiler.

generate_hardware_config() → dict[str, Any]

Generate a valid hardware configuration describing the quantum device.

Returns:

The hardware configuration file used for compiling from the quantum-device layer to a hardware backend.

generate_device_config() → qblox_scheduler.backends.graph_compilation.DeviceCompilationConfig

Generate a device config.

This config is used to compile from the composite to the quantum-device layer.

generate_hardware_compilation_config() → qblox_scheduler.backends.types.common.HardwareCompilationConfig | None

Generate a hardware compilation config.

The compilation config is used to compile from the quantum-device to the control-hardware layer.

get_element(name: str) → qblox_scheduler.device_under_test.device_element.DeviceElement

Return a DeviceElement by name.

Parameters:

name – The element name.

Returns:

: The element.

Raises:

KeyError – If key name is not present in self.elements.

add_element(element: qblox_scheduler.device_under_test.device_element.DeviceElement) → None

Add an element to the elements collection.

Parameters:

element – The element to add.

Raises:

• ValueError – If an element with a duplicated name is added to the collection.

• TypeError – If element is not an instance of the base element.

remove_element(name: str) → None

Removes an element by name.

Parameters:

name – The element name. Has to follow the convention "{element_0}_{element_1}".

get_edge(name: str) → qblox_scheduler.device_under_test.edge.Edge

Returns an edge by name.

Parameters:

name – The edge name. Has to follow the convention "{element_0}_{element_1}".

Returns:

: The edge.

Raises:

KeyError – If key name is not present in self.edges.

add_edge(edge: qblox_scheduler.device_under_test.edge.Edge) → None

Add the edges.

Parameters:

edge – The edge to add.

remove_edge(name: str) → None

Remove an edge by name.

Parameters:

name – The edge name connecting the elements. Has to follow the convention "{element_0}_{element_1}".

classmethod from_json_file(filename: str | pathlib.Path) → Self

Read JSON data from a file and convert it to an instance of the attached class.

class TriggerCondition

Bases: StrEnum

Comparison condition for the thresholded trigger count acquisition.

LESS_THAN = 'less_than'

GREATER_THAN_EQUAL_TO = 'greater_than_equal_to'

class MockLocalOscillator(name: str)

Bases: qcodes.instrument.instrument.Instrument

A class representing a dummy Local Oscillator.

Parameters:

name – QCoDeS name of the instrument.

_add_qcodes_parameters_dummy() → None

Use to fake communications.

class Acquisition(name: str)

Bases: qblox_scheduler.operations.operation.Operation

An operation representing data acquisition at the quantum-device abstraction layer.

An Acquisition must consist of (at least) an AcquisitionProtocol specifying how the acquired signal is to be processed, and an AcquisitionLabel and AcquisitionIndex specifying where the acquired data is to be stored in the RawDataset.

N.B. This class helps differentiate an acquisition operation from the regular operations. This enables us to use plot_acquisition_operations() to highlight acquisition pulses in the pulse diagrams.

class DualThresholdedTriggerCount(port: str, clock: str, duration: float, threshold_low: int, threshold_high: int, *, label_low: str | None = None, label_mid: str | None = None, label_high: str | None = None, label_invalid: str | None = None, acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True)

Bases: Acquisition

Thresholded trigger count protocol that uses two thresholds.

Four outcomes are possible for this measurement, and each of those four can be assigned a label to use in a ConditionalOperation:

• “low” if counts < threshold_low,

• “mid” if threshold_low <= counts < threshold_high,

• “high” if counts >= threshold_high,

• “invalid” if the counts are invalid (can occur in very rare cases, e.g. when the counter overflows).

The returned acquisition data is the raw number of counts.

Important

The exact duration of this operation, and the possible bin modes may depend on the control hardware. Please consult your hardware vendor’s Reference guide for more information.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The duration of the operation in seconds.

• threshold_low – The lower counts threshold of the ThresholdedTriggerCount acquisition.

• threshold_high – The upper counts threshold of the ThresholdedTriggerCount acquisition.

• label_low – The label that can be used to link a result of counts < threshold_low to a ConditionalOperation, by default None.

• label_mid – The label that can be used to link a result of threshold_low <= counts < threshold_high to a ConditionalOperation, by default None.

• label_high – The label that can be used to link a result of counts >= threshold_high to a ConditionalOperation, by default None.

• label_invalid – The label that can be used to link an invalid counts result (e.g. a counter overflow) to a ConditionalOperation, by default None.

• feedback_trigger_condition – The comparison condition (greater-equal, less-than) for the ThresholdedTriggerCount acquisition.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a qubit idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

class IQIntegration(port: str, clock: str, duration: float | None = None, weights_a: list[complex] | list[float] | numpy.ndarray | None = None, weights_b: list[complex] | list[float] | numpy.ndarray | None = None, interpolation: str = 'linear', acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True)

Bases: Acquisition

Weighted integration acquisition protocol returning integrated I and Q as one complex number.

If no weights (weights_a/weights_b) are given, a square window is used as the integration weights (single sideband integration); duration then defaults to 50 ms and may not exceed 50 ms. If weights are given, the specified weights are used for the integration (with interpolation where necessary), and the acquisition length is limited to the length of the weight memory of 16384 ns.

Weights are applied as:

\[\widetilde{A} = \int \mathrm{Re}(S(t))\cdot W_A(t) \mathrm{d}t\]

\[\widetilde{B} = \int \mathrm{Im}(S(t))\cdot W_B(t) \mathrm{d}t\]

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The acquisition duration in seconds. When no weights are given, defaults to 50 ms and is capped at 50 ms. When weights are given, duration is the time the weight arrays span (the weight sampling rate is derived as len(weights_a) / duration); if omitted, the weights are assumed to be sampled at 1 GHz, so duration = len(weights_a) / 1e9 (capped at 16384 ns).

• weights_a – The list of complex values used as weights \(W_A(t)\) on the incoming signal’s real part. By default None, in which case a square window is used (single sideband integration).

• weights_b – The list of complex values used as weights \(W_B(t)\) on the incoming signal’s imaginary part. By default None.

• interpolation – Specifies the kind of interpolation to use. The string has to be one of ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, or ‘next’. ‘zero’, ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of zeroth, first, second or third order; ‘previous’ and ‘next’ simply return the previous or next value of the point; ‘nearest-up’ and ‘nearest’ differ when interpolating half-integers (e.g. 0.5, 1.5) in that ‘nearest-up’ rounds up and ‘nearest’ rounds down. Default is ‘linear’. Only used if weights are given.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

Raises:

ValueError – If exactly one of weights_a/weights_b is given, or if the (derived) duration exceeds the allowed maximum.

_DEFAULT_DURATION_NO_WEIGHTS = 0.05

_MAX_DURATION_NO_WEIGHTS = 0.05

_MAX_DURATION_WEIGHTED = 1.6384e-05

_DEFAULT_WEIGHTS_SAMPLING_RATE = 1000000000.0

class IQIntegrationMixed(port: str, clock: str, weights_a: list[complex] | numpy.ndarray, weights_b: list[complex] | numpy.ndarray, duration: float | None = None, interpolation: str = 'linear', acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True)

Bases: IQIntegration

Subclass of IQIntegration returning a real number, combining weighted I and Q.

Returns the integrated (I*w_a + Q*w_b) based on the integration weights, rather than the separate I and Q components as one complex number.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• weights_a – The list of complex values used as weights \(A(t)\) on the incoming complex signal.

• weights_b – The list of complex values used as weights \(B(t)\) on the incoming complex signal.

• duration – The time in seconds spanned by the weight arrays. If omitted, the weights are assumed to be sampled at 1 GHz. Capped at 16384 ns.

• interpolation – Specifies the kind of interpolation to use. The string has to be one of ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, or ‘next’. ‘zero’, ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of zeroth, first, second or third order; ‘previous’ and ‘next’ simply return the previous or next value of the point; ‘nearest-up’ and ‘nearest’ differ when interpolating half-integers (e.g. 0.5, 1.5) in that ‘nearest-up’ rounds up and ‘nearest’ rounds down. Default is ‘linear’. Only used if weights are given.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

class ThresholdedIQIntegration(port: str, clock: str, duration: float, acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True, feedback_trigger_label: str | None = None, acq_rotation: float = 0, acq_threshold: float = 0)

Bases: Acquisition

Acquisition protocol allowing to control rotation and threshold.

This acquisition protocol is similar to the IQIntegration acquisition protocol, but the complex result is now rotated and thresholded to produce a “0” or a “1”, as controlled by the parameters for rotation angle <device_element>.measure.acq_rotation and threshold value <device_element>.measure.acq_threshold in the device configuration (see example below).

The rotation angle and threshold value for each qubit can be set through the device configuration.

Note

Thresholded acquisition is currently only supported by the Qblox backend.

Examples

from qblox_scheduler.schedules.schedule import TimeableSchedule
from qblox_applications.qblox_scheduler.superconducting.transmon.device_element import (
    transmon_element,
)
from qblox_scheduler import ThresholdedIQIntegration

# set up qubit
device_element = transmon_element.BasicTransmonElement("q0")
device_element.clock_freqs.readout = 8.0e9

# set rotation and threshold value
rotation, threshold = 20, -0.1
device_element.measure.acq_rotation = rotation
device_element.measure.acq_threshold = threshold

# basic schedule
schedule = TimeableSchedule("thresholded acquisition")
schedule.add(ThresholdedIQIntegration(port="q0:res", clock="q0.ro", duration=1e-6))

{'name': '62a6e084-37a0-4a39-a8dc-ee4b9d608a2e', 'operation_id': '4346211738683366866', 'timing_constraints': [TimingConstraint(ref_schedulable=None, ref_pt=None, ref_pt_new=None, rel_time=0)], 'label': '62a6e084-37a0-4a39-a8dc-ee4b9d608a2e'}

Parameters:

• port (str) – The acquisition port.

• clock (str) – The clock used to demodulate the acquisition.

• duration (float) – The acquisition duration in seconds.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

• feedback_trigger_label (str) – The label corresponding to the feedback trigger, which is mapped by the compiler to a feedback trigger address on hardware, by default None.

class ThresholdedIQIntegrationMixed(port: str, clock: str, weights_a: list[complex] | numpy.ndarray, weights_b: list[complex] | numpy.ndarray, duration: float | None = None, interpolation: str = 'linear', acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True, feedback_trigger_label: str | None = None, acq_threshold: float = 0)

Bases: IQIntegrationMixed

Subclass of IQIntegrationMixed but Thresholded.

Acquisition protocol allowing to control rotation and threshold.

This acquisition protocol is similar to the IQIntegration acquisition protocol, but the complex result is now rotated and thresholded to produce a “0” or a “1”, as controlled by the parameters for rotation angle <qubit>.measure.acq_rotation and threshold value <qubit>.measure.acq_threshold in the device configuration (see example below).

The rotation angle and threshold value for each qubit can be set through the device configuration.

Note

Thresholded acquisition is currently only supported by the Qblox backend.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• weights_a – The list of complex values used as weights \(A(t)\) on the incoming complex signal.

• weights_b – The list of complex values used as weights \(B(t)\) on the incoming complex signal.

• duration – The time in seconds spanned by the weight arrays. If omitted, the weights are assumed to be sampled at 1 GHz. Capped at 16384 ns.

• interpolation – Specifies the kind of interpolation to use. The string has to be one of ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, or ‘next’. ‘zero’, ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of zeroth, first, second or third order; ‘previous’ and ‘next’ simply return the previous or next value of the point; ‘nearest-up’ and ‘nearest’ differ when interpolating half-integers (e.g. 0.5, 1.5) in that ‘nearest-up’ rounds up and ‘nearest’ rounds down. Default is ‘linear’. Only used if weights are given.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

• feedback_trigger_label (str) – The label corresponding to the feedback trigger, which is mapped by the compiler to a feedback trigger address on hardware, by default None.

class ThresholdedTriggerCount(port: str, clock: str, duration: float, threshold: int, *, feedback_trigger_label: str | None = None, feedback_trigger_condition: str | qblox_scheduler.enums.TriggerCondition = TriggerCondition.GREATER_THAN_EQUAL_TO, acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True)

Bases: Acquisition

Thresholded trigger counting acquisition protocol returning the comparison result with a threshold.

If the number of triggers counted is less than the threshold, a 0 is returned, otherwise a 1.

The analog threshold for registering a single count is set in the hardware configuration.

Important

The exact duration of this operation, and the possible bin modes may depend on the control hardware. Please consult your hardware vendor’s Reference guide for more information.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The duration of the operation in seconds.

• threshold – The threshold of the ThresholdedTriggerCount acquisition.

• feedback_trigger_label – The label corresponding to the feedback trigger, which is mapped by the compiler to a feedback trigger address on hardware, by default None. Note: this label is merely used to link this acquisition together with a ConditionalOperation. It does not affect the acquisition result.

• feedback_trigger_condition – The comparison condition (greater-equal, less-than) for the ThresholdedTriggerCount acquisition.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a qubit idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

class Timetag(duration: float, port: str, clock: str = DigitalClockResource.IDENTITY, acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True, time_source: qblox_scheduler.enums.TimeSource | str = TimeSource.FIRST, time_ref: qblox_scheduler.enums.TimeRef | str = TimeRef.START, time_ref_port: str | None = None, fine_start_delay: float = 0, fine_end_delay: float = 0)

Bases: Acquisition

Acquire a single timetag per acquisition index.

Important

The exact duration of this operation, and the possible bin modes may depend on the control hardware. Please consult your hardware vendor’s Reference guide for more information.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The acquisition duration in seconds.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

• time_source –

  Selects the timetag data source for this acquisition type. String enumeration, one of:

  • first (default): record the first timetag in the window.

  • second: record the second timetag in the window. Can be used to measure pulse distance when combined with first as reference.

  • last: record the last timetag in the window.

• time_ref –

  Selects the time reference that the timetag is recorded in relation to. String enumeration, one of:

  • start (default): record relative to the start of the window.

  • end: record relative to the end of the window. Note that this always yields a negative timetag.

  • first: record relative to the first timetag in the window.

  • timestamp: record relative to the timestamp marked using the Timestamp operation.

  • port: record relative to the timetag measured on another port. If this option is used, the time_ref_port argument must be specified as well. The acquisition operation that is measuring the timetag on the other port must end before or at the same time as this acquisition operation.

• time_ref_port – If the port time reference is used, time_ref_port specifies the port on which the other acquisition is executed.

• fine_start_delay – Delays the start of the acquisition by the given amount in seconds. Does not delay the start time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

• fine_end_delay – Delays the end of the pulse by the given amount. Does not delay the end time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

class TimetagTrace(duration: float, port: str, clock: str = DigitalClockResource.IDENTITY, acq_label: collections.abc.Hashable = 0, coords: dict | None = None, time_ref: qblox_scheduler.enums.TimeRef | str = TimeRef.START, time_ref_port: str | None = None, fine_start_delay: float = 0, fine_end_delay: float = 0)

Bases: Acquisition

The TimetagTrace acquisition protocol records timetags within an acquisition window.

Important

The exact duration of this operation, and the possible bin modes may depend on the control hardware. Please consult your hardware vendor’s Reference guide for more information.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The acquisition duration in seconds.

• acq_label – The data channel in which the acquisition is stored, is by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• time_ref –

  Selects the time reference that the timetag is recorded in relation to. String enumeration, one of:

  • start (default): record relative to the start of the window.

  • end: record relative to the end of the window. Note that this always yields a negative timetag.

  • first: syntactic sugar for first#, where # is the current channel.

  • timestamp: record relative to the timestamp marked using the Timestamp operation.

  • port: record relative to the timetag measured on another port. If this option is used, the time_ref_port argument must be specified as well. The acquisition operation that is measuring the timetag on the other port must end before or at the same time as this acquisition operation.

• time_ref_port – If the port time reference is used, time_ref_port specifies the port on which the other acquisition is executed.

• fine_start_delay – Delays the start of the acquisition by the given amount in seconds. Does not delay the start time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

• fine_end_delay – Delays the end of the pulse by the given amount in seconds. Does not delay the end time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

class Trace(duration: float, port: str, clock: str, acq_label: collections.abc.Hashable = 0, coords: dict | None = None)

Bases: Acquisition

The Trace acquisition protocol measures a signal s(t).

Only processing performed is rescaling and adding units based on a calibrated scale. Values are returned as a raw trace (numpy array of float datatype). Length of this array depends on the sampling rate of the acquisition device.

Important

The exact duration of this operation, and the possible bin modes may depend on the control hardware. Please consult your hardware vendor’s Reference guide for more information.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The acquisition duration in seconds.

• acq_label – The data channel in which the acquisition is stored, is by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

class TriggerCount(port: str, clock: str, duration: float, acq_label: collections.abc.Hashable = 0, coords: dict | None = None, average_segment_repetitions: bool = True, fine_start_delay: float = 0, fine_end_delay: float = 0)

Bases: Acquisition

Trigger counting acquisition protocol returning an integer.

The trigger acquisition mode is used to measure how many times the trigger level is surpassed. The level is set in the hardware configuration.

Important

The exact duration of this operation, and the possible bin modes may depend on the control hardware. Please consult your hardware vendor’s Reference guide for more information.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The duration of the operation in seconds.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

• fine_start_delay – Delays the start of the acquisition by the given amount in seconds. Does not delay the start time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

• fine_end_delay – Delays the end of the pulse by the given amount in seconds. Does not delay the end time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

class TriggerCountDistribution(port: str, clock: str, duration: float, acq_label: collections.abc.Hashable = 0, coords: dict | None = None, fine_start_delay: float = 0, fine_end_delay: float = 0)

Bases: Acquisition

Trigger counting distribution acquisition protocol returning an integer.

The trigger acquisition mode is used to measure how many times the trigger level is surpassed. The level is set in the hardware configuration.

Parameters:

• port – The acquisition port.

• clock – The clock used to demodulate the acquisition.

• duration – The duration of the operation in seconds.

• acq_label – The data channel in which the acquisition is stored, by default 0. Describes the “where” information of the measurement, which typically corresponds to a device element idx.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• fine_start_delay – Delays the start of the acquisition by the given amount in seconds. Does not delay the start time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

• fine_end_delay – Delays the end of the pulse by the given amount in seconds. Does not delay the end time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the acquisition window by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

class ConditionalReset(qubit_name: str, name: str = 'conditional_reset', **kwargs)

Bases: qblox_scheduler.schedules.schedule.TimeableSchedule

Reset a qubit to the \(|0\rangle\) state.

The ConditionalReset gate is a conditional gate that first measures the state of the device element using an ThresholdedIQIntegration operation and then performs a \(\pi\) rotation on the condition that the measured state is \(|1\rangle\). If the measured state is in \(|0\rangle\), the hardware will wait the same amount of time the \(\pi\) rotation would’ve taken to ensure that total execution time of ConditionalReset is the same regardless of the measured state.

Note

The total time of the ConditionalReset is the sum of

1. integration time (<device_element>.measure.integration_time)

2. acquisition delay (<device_element>.measure.acq_delay)

3. trigger delay (364ns)

4. pi-pulse duration (<device_element>.rxy.duration)

5. idle time (4ns)

Note

Due to current hardware limitations, overlapping conditional resets might not work correctly if multiple triggers are sent within a 364ns window. See sec-qblox-conditional-playback for more information.

Note

ConditionalReset is currently implemented as a subschedule, but can be added to an existing schedule as if it were a gate. See examples below.

Parameters:

• name (str) – The name of the conditional subschedule, by default “conditional_reset”.

• qubit_name (str) – The name of the device element to reset to the \(|0\rangle\) state.

• **kwargs – Additional keyword arguments are passed to Measure. e.g. acq_label, acq_index, and bin_mode.

Examples

from qblox_scheduler import Schedule
from qblox_scheduler import ConditionalReset

schedule = Schedule("example schedule")
schedule.add(ConditionalReset("q0"))

clone() → ConditionalReset

Clone this schedule into a separate independent schedule.

class ConditionalOperation(body: qblox_scheduler.operations.operation.Operation | qblox_scheduler.schedules.schedule.TimeableSchedule | qblox_scheduler.schedule.Schedule, feedback_trigger_label: str | None = None, hardware_buffer_time: float = constants.MIN_TIME_BETWEEN_OPERATIONS * 1e-09)

Bases: ControlFlowOperation

Conditional over another operation.

If a preceding thresholded acquisition on feedback_trigger_label results in a “1”, the body will be executed, otherwise it will generate a wait time that is equal to the time of the subschedule, to ensure the absolute timing of later operations remains consistent.

Parameters:

• body – Operation to be conditionally played

• feedback_trigger_label – Name of the device element on which the body will be conditioned

• hardware_buffer_time – Time buffer, by default the minimum time between operations on the hardware

Example

A conditional reset can be implemented as follows:

# relevant imports
from qblox_scheduler import Schedule
from qblox_scheduler import ConditionalOperation, Measure, X

# define conditional reset as a Schedule
conditional_reset = Schedule("conditional reset")
conditional_reset.add(Measure("q0", feedback_trigger_label="q0"))
conditional_reset.add(
    ConditionalOperation(body=X("q0"), feedback_trigger_label="q0"),
    rel_time=364e-9,
)

{'name': '4f9643b6-7faf-4e96-b3ba-d09a5ebb911e', 'operation_id': '-4783262883086729782', 'timing_constraints': [TimingConstraint(ref_schedulable=None, ref_pt=None, ref_pt_new=None, rel_time=3.64e-07)], 'label': '4f9643b6-7faf-4e96-b3ba-d09a5ebb911e'}

Added in version 0.22.0: For some hardware specific implementations, a hardware_buffer_time might be required to ensure the correct timing of the operations. This will be added to the duration of the body to prevent overlap with other operations.

property body: qblox_scheduler.operations.operation.Operation | qblox_scheduler.schedules.schedule.TimeableSchedule

Body of a control flow.

property duration: float

Duration of a control flow.

class LoopStrategy

Bases: qblox_scheduler.enums.StrEnum

Strategy to use for implementing loops.

REALTIME: Use native loops. UNROLLED: Unroll loop at compilation time into separate instructions.

REALTIME = 'realtime'

UNROLLED = 'unrolled'

class DType

Bases: qblox_scheduler.enums.StrEnum

Data type of a variable or expression.

NUMBER = 'number'

A number, corresponding to 1, 2, 3, etc.

AMPLITUDE = 'amplitude'

An amplitude, corresponding to 0.1, 0.2, 0.3, etc. in dimensionless units ranging from -1 to 1.

DRAG_BETA = 'drag_beta'

Beta parameter of the drag pulse. Units of seconds.

TIME = 'time'

A time, corresponding to 20e-9, 40e-9, 60e-9, etc. in seconds.

FREQUENCY = 'frequency'

A frequency, corresponding to 1e9, 2e9, 3e9, etc. in Hz.

PHASE = 'phase'

A phase, corresponding to e.g. 0, 30, 60, 90, etc. in degrees ranging from 0 to 360.

COMPLEX = 'complex'

A complex amplitude, corresponding to e.g., 0.1 + 0.2j, 0.3 + 0.4j, etc, in dimensionless units ranging from -1 to 1.

is_timing_sensitive() → bool

Whether an expression of this type affects timing.

class CNOT(qC: str, qT: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

Conditional-NOT gate, a common entangling gate.

Performs an X gate on the target qubit qT conditional on the state of the control qubit qC.

This operation can be represented by the following unitary:

\[\begin{split}\mathrm{CNOT} = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 1 & 0 \\ \end{bmatrix}\end{split}\]

Parameters:

• qC – The control device element.

• qT – The target device element

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class CZ(qC: str, qT: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

Conditional-phase gate, a common entangling gate.

Performs a Z gate on the target device element qT conditional on the state of the control device element qC.

This operation can be represented by the following unitary:

\[\begin{split}\mathrm{CZ} = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & -1 \\ \end{bmatrix}\end{split}\]

Parameters:

• qC – The control device element.

• qT – The target device element

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class X90(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 90 degrees around the X-axis.

It can be described as Rxy gate with theta=90 and phi=0

Defined by the unitary:

\[\begin{split}X90 = R_{X90} = \frac{1}{\sqrt{2}}\begin{bmatrix} 1 & -i \\ -i & 1 \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Y90(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 90 degrees around the Y-axis.

It can be described as Rxy gate with theta=90 and phi=90

Defined by the unitary:

\[\begin{split}Y90 = R_{Y90} = \frac{1}{\sqrt{2}}\begin{bmatrix} 1 & -1 \\ 1 & 1 \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Z90(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 90 degrees around the Z-axis.

This operation can be represented by the following unitary:

\[\begin{split}Z90 = R_{Z90} = e^{-\frac{\pi/2}{2}}S = e^{-\frac{\pi/2}{2}}\sqrt{Z} = \frac{1}{\sqrt{2}}\begin{bmatrix} 1-i & 0 \\ 0 & 1+i \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class H(*qubits: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit Hadamard gate.

Note that the gate uses \(R_z(\pi) = -iZ\), adding a global phase of \(-\pi/2\). This operation can be represented by the following unitary:

\[\begin{split}H = Y90 \cdot Z = \frac{-i}{\sqrt{2}}\begin{bmatrix} 1 & 1 \\ 1 & -1 \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Measure(*qubits: str, acq_label: collections.abc.Hashable | None = None, coords: dict | None = None, acq_protocol: Literal['IQIntegration', 'IQIntegrationMixed', 'ThresholdedIQIntegration', 'ThresholdedIQIntegrationMixed', 'Timetag', 'TimetagTrace', 'Trace', 'TriggerCount', 'ThresholdedTriggerCount', 'SSBIntegrationComplex', 'NumericalSeparatedWeightedIntegration'] | None = None, average_segment_repetitions: bool = True, feedback_trigger_label: str | None = None, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A projective measurement in the Z-basis.

The measurement is compiled according to the type of acquisition specified in the device configuration.

Note

Strictly speaking this is not a gate as it can not be described by a unitary.

Parameters:

• qubits – The device elements you want to measure.

• acq_label – Only for special use cases. By default (if None): the acquisition label specified in the device element is used. If set, this acquisition label is used for this measurement.

• coords – Coords for the acquisition. These coordinates for the measured value for this operation appear in the retrieved acquisition data. For example coords={"amp": 0.1} has the effect, that the measured value for this acquisition will be associated with amp==0.1. By default None, no coords are added.

• acq_protocol ("IQIntegration" | "IQIntegrationMixed" | "Trace" | "TriggerCount" | "ThresholdedIQIntegration" | "ThresholdedIQIntegrationMixed" | None, Optional) – Acquisition protocols that are supported. If None is specified, the default protocol is chosen based on the device and backend configuration. By default None.

• average_segment_repetitions – Specifies whether acquisitions in each segment are averaged or kept separately/appended.

• feedback_trigger_label (str) – The label corresponding to the feedback trigger, which is mapped by the compiler to a feedback trigger address on hardware, by default None.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Reset(*qubits: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

Reset a qubit to the \(|0\rangle\) state.

The Reset gate is an idle operation that is used to initialize one or more qubits.

Note

Strictly speaking this is not a gate as it can not be described by a unitary.

Examples

The operation can be used in several ways:

from qblox_scheduler import Reset

reset_1 = Reset("q0")
reset_2 = Reset("q1", "q2")
reset_3 = Reset(*[f"q{i}" for i in range(3, 6)])

Parameters:

• qubits – The device element(s) to reset. NB one or more device element can be specified, e.g., Reset("q0"), Reset("q0", "q1", "q2"), etc..

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Rxy(theta: float | qblox_scheduler.operations.expressions.Expression, phi: float | qblox_scheduler.operations.expressions.Expression, qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation around an axis in the equator of the Bloch sphere.

This operation can be represented by the following unitary as defined in https://doi.org/10.1109/TQE.2020.2965810:

\[\begin{split}\mathsf {R}_{xy} \left(\theta, \varphi\right) = \begin{bmatrix} \textrm {cos}(\theta /2) & -ie^{-i\varphi }\textrm {sin}(\theta /2) \\ -ie^{i\varphi }\textrm {sin}(\theta /2) & \textrm {cos}(\theta /2) \end{bmatrix}\end{split}\]

Parameters:

• theta – Rotation angle in degrees, will be casted to the [-180, 180) domain.

• phi – Phase of the rotation axis, will be casted to the [0, 360) domain.

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Rz(theta: float, qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation about the Z-axis of the Bloch sphere.

This operation can be represented by the following unitary as defined in https://www.quantum-inspire.com/kbase/rz-gate/:

\[\begin{split}\mathsf {R}_{z} \left(\theta\right) = \begin{bmatrix} e^{-i\theta/2} & 0 \\ 0 & e^{i\theta/2} \end{bmatrix}\end{split}\]

Parameters:

• theta – Rotation angle in degrees, will be cast to the [-180, 180) domain.

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

property qubit: str

Target device element.

property theta: float

Rotation angle in degrees, will be cast to the [-180, 180) domain.

class S(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 90 degrees around the Z-axis.

This implements an \(S\) gate up to a global phase. While this operation is identical to the Z90 operation, it is kept as an independent class to allow explicit definition in device configurations.

This operation can be represented by the following unitary:

\[\begin{split}R_{Z90} = e^{-i\frac{\pi}{4}}S = e^{-i\frac{\pi}{4}}\sqrt{Z} = \frac{1}{\sqrt{2}}\begin{bmatrix} 1-i & 0 \\ 0 & 1+i \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class SDagger(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of -90 degrees around the Z-axis.

Implements \(S^\dagger\) up to a global phase.

This operation can be represented by the following unitary:

\[\begin{split}R_{Z270} = e^{\frac{\pi}{4}}S^\dagger = e^{\frac{\pi}{4}}\sqrt{Z}^\dagger = \frac{1}{\sqrt{2}}\begin{bmatrix} 1+i & 0 \\ 0 & 1-i \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class T(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 45 degrees around the Z-axis.

Implements \(T\) up to a global phase.

This operation can be represented by the following unitary:

\[\begin{split}R_{Z45} = e^{-\frac{\pi}{8}}T = e^{-\frac{\pi}{8}}\begin{bmatrix} 1 & 0 \\ 0 & \frac{1+i}{\sqrt{2}} \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class TDagger(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of -45 degrees around the Z-axis.

Implements \(T^\dagger\) up to a global phase.

This operation can be represented by the following unitary:

\[\begin{split}R_{Z315} = e^{\frac{\pi}{8}}T^\dagger = e^{\frac{\pi}{8}}\begin{bmatrix} 1 & 0 \\ 0 & \frac{1-i}{\sqrt{2}} \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class X(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 180 degrees around the X-axis.

This operation can be represented by the following unitary:

\[\begin{split}X180 = R_{X180} = \begin{bmatrix} 0 & -i \\ -i & 0 \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Y(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 180 degrees around the Y-axis.

It can be described as Rxy gate with theta=180 and phi=90

Defined by the unitary:

\[\begin{split}Y180 = R_{Y180} = \begin{bmatrix} 0 & -1 \\ 1 & 0 \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class Z(qubit: str, **device_overrides)

Bases: qblox_scheduler.operations.operation.Operation

A single qubit rotation of 180 degrees around the Z-axis.

Note that the gate implements \(R_z(\pi) = -iZ\), adding a global phase of \(-\pi/2\). This operation can be represented by the following unitary:

\[\begin{split}Z180 = R_{Z180} = -iZ = e^{-\frac{\pi}{2}}Z = \begin{bmatrix} -i & 0 \\ 0 & i \\ \end{bmatrix}\end{split}\]

Parameters:

• qubit – The target device element.

• device_overrides – Device level parameters that override device configuration values when compiling from composite to device level.

class InlineQ1ASM(program: str, duration: float, port: str, clock: str, *, waveforms: dict | None = None, safe_labels: bool = True)

Bases: qblox_scheduler.operations.operation.Operation

Initialize an InlineQ1ASM operation.

This method sets up an operation that contains inline Q1ASM code

to be injected directly into a Schedule.

All comments in the program will be prefixed with an ‘[inline]’ prefix to help identify the inline assembly within the sequencer program.

When using safe labels, then all labels included in the input program will get a prefix of ‘inj<digits>_’. By default, safe labels are always used. Labels in comments will not be modified.

Parameters:

• program – The Q1ASM program to be injected.

• duration – The duration of the operation in seconds.

• port – The port on which the operation is to be executed.

• clock – The clock associated with the operation.

• waveforms – Dictionary containing waveform information, by default None.

• safe_labels – Flag to indicate if safe labels should be used, by default True.

Returns:

None

Notes

Warning

When using safe_labels=False then all labels in the sequencer program are accessible from inside the inline Q1ASM injection, and so can be jumped to or overwritten. Disabling this feature is available for debugging and advanced compilation strategies only.

_name = 'InlineQ1ASM'

program

_duration

port

clock

waveforms

safe_labels = True

property name: str

Return the name of the operation.

property duration: float

The duration of this operation.

get_used_port_clocks() → set[tuple[str, str]]

Extracts which port-clock combinations are used in this operation.

Returns:

: All (port, clock) combinations this operation uses.

class LatchReset(portclock: tuple[str, str], duration: float = 4e-09)

Bases: qblox_scheduler.operations.pulse_library._PulseOperationBase

Operation that resets the feedback trigger addresses from the hardware.

Currently only implemented for Qblox backend, refer to ResetFeedbackTriggersStrategy for more details.

Parameters:

• portclock – Tuple containing the port that the pulse should be played on, and the clock used to (de)modulate the pulse (by default the baseband clock).

• duration – The pulse duration in seconds.

class SimpleNumericalPulse(samples: numpy.ndarray | list, port: str, clock: str = BasebandClockResource.IDENTITY, amplitude: complex | float | qblox_scheduler.operations.expressions.Expression | collections.abc.Sequence[complex | float | qblox_scheduler.operations.expressions.Expression] = 1.0, **signal_args: Unpack[qblox_scheduler.operations.pulse_library.PulseSignalArgs])

Bases: qblox_scheduler.operations.pulse_library.NumericalPulse

Wrapper on top of NumericalPulse to provide a simple interface for creating a pulse where the samples correspond 1:1 to the produced waveform, without needing to specify the time samples.

Parameters:

• samples – An array of (possibly complex) values specifying the shape of the pulse.

• port – The port that the pulse should be played on.

• clock – Clock used to (de)modulate the pulse. By default the baseband clock.

• gain – Gain factor between -1 and 1 that multiplies with the samples, by default 1.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

Example

from qblox_scheduler import (
    SimpleNumericalPulse
)
from qblox_scheduler.schedules.schedule import TimeableSchedule

waveform = [0.1,0.2,0.2,0.3,0.5,0.4]

schedule = TimeableSchedule("")
schedule.add(SimpleNumericalPulse(waveform, port="q0:out"))

{'name': '8129f80f-bc55-49d3-a502-75f4e66b3d2d', 'operation_id': '773978503107648205', 'timing_constraints': [TimingConstraint(ref_schedulable=None, ref_pt=None, ref_pt_new=None, rel_time=0)], 'label': '8129f80f-bc55-49d3-a502-75f4e66b3d2d'}

arange(stop: float | int, dtype: qblox_scheduler.operations.expressions.DType) → LinearDomain

arange(start: float | int, stop: float, dtype: qblox_scheduler.operations.expressions.DType) → LinearDomain

arange(start: float | int, stop: float | int, step: float | int, dtype: qblox_scheduler.operations.expressions.DType) → LinearDomain

Linear range of values to loop over, specified with a start value, an exclusive stop value and a step size.

Parameters:

• start – Start of interval. The interval includes this value.

• stop – End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.

• step – Spacing between values. For any output out, this is the distance between two adjacent values, out[i+1] - out[i].

• dtype – Data type of the linear domain.

linspace(start: complex | float | int, stop: complex | float | int, num: int, dtype: qblox_scheduler.operations.expressions.DType) → LinearDomain

Linear range of values to loop over, specified with a start value, an inclusive stop value and the number of linearly spaced points to generate.

Parameters:

• start – The starting value of the sequence.

• stop – The end value of the sequence.

• num – Number of samples to generate. Must be non-negative.

• dtype – Data type of the linear domain.

class Operation(name: str)

Bases: qblox_scheduler.json_utils.JSONSchemaValMixin, collections.UserDict

A representation of quantum circuit operations.

The Operation class is a JSON-compatible data structure that contains information on how to represent the operation on the composite and/or the quantum-device layer. It also contains information on where the operation should be applied: the Resource s used.

An operation always has the following attributes:

• duration (float): duration of the operation in seconds (can be 0).

• hash (str): an auto generated unique identifier.

• name (str): a readable identifier, does not have to be unique.

An Operation can contain information on several levels of abstraction. This information is used when different representations are required. Note that when initializing an operation not all of this information needs to be available as operations are typically modified during the compilation steps.

Tip

qblox_scheduler comes with a gate_library and a pulse_library , both containing common operations.

JSON schema of a valid Operation

qblox://operation
JSON schema for a qblox sequencer operation.
type	object
properties
name	type	string
gate_info	type	object
	properties
	unitary	A unitary matrix describing the operation.
	operation_type	Defines what class of operations this gate refers to (e.g. Rxy, CZ etc.).
		type	string
	qubits	Deprecated. A list of strings indicating the qubits the gate acts on. Valid qubits are strings that appear in the device_config.json file.
		type	array
	device_elements	A list of strings indicating the device_elements the gate acts on. Valid device_elements are strings that appear in the device_config.json file.
		type	array
	symmetric	A boolean to indicate whether a two device elements is symmetric. This is used in the device config compilation stage. By default, it is set as False
		type	boolean
	tex	latex snippet for plotting
		type	string
	plot_func	reference to a function for plotting this operation. If not specified, defaults to using gate_box().
		type	string / null
	additionalProperties	True
pulse_info	Info to generate an individual pulse.
	type	object
	properties
	port	A string specifying the port used by the pulse.
		type	string / null
	clock	A string specifying the clock used to modulate the pulse.
		type	string
	wf_func	reference to a function to generate the pulse of this operation.
		type	string / null
	duration	The duration of the pulse in seconds.
		type	number
acquisition_info	Info to generate an individual acquisition.
	type	object
	properties
	port	A string specifying the port used by the acquisition.
		type	string
	clock	A string specifying the clock used to demodulate the acquisition.
		type	string
	duration	The duration of the acquisition in seconds.
		type	number
	acq_label	The acquisition label to use.
		type	number
	acq_return_type	Describes the return type of an acquisition performed using this protocol.
	additionalProperties	True
logic_info	Not Implemented.
statement_info	type	object
	properties
	variable	type	object
	value	type	object / number
	used_port_clocks	type	array
	used_clocks	type	array
	additionalProperties	True
additionalProperties	False

Note

Two different Operations containing the same information generate the same hash and are considered identical.

schema_filename = 'operation.json'

_class_signature = None

_duration: float = 0

clone() → Operation

Clone this operation into a new independent operation.

substitute(substitutions: dict[qblox_scheduler.operations.expressions.Expression, qblox_scheduler.operations.expressions.Expression | int | float | complex]) → Operation

Substitute matching expressions in operand, possibly evaluating a result.

property name: str

Return the name of the operation.

property duration: float

Determine operation duration from pulse_info.

If the operation contains no pulse info, it is assumed to be ideal and have zero duration.

property hash: str

A hash based on the contents of the Operation.

Needs to be a str for easy compatibility with json.

classmethod _get_signature(parameters: dict) → str

Returns the constructor call signature of this instance for serialization.

The string constructor representation can be used to recreate the object using eval(signature).

Parameters:

parameters (dict) – The current data dictionary.

Returns:

:

add_gate_info(gate_operation: Operation) → None

Updates self.data[‘gate_info’] with contents of gate_operation.

Parameters:

gate_operation – an operation containing gate_info.

add_device_representation(device_operation: Operation) → None

Adds device-level representation details to the current operation.

Parameters:

device_operation – an operation containing the pulse_info and/or acquisition info describing how to represent the current operation at the quantum-device layer.

get_used_port_clocks() → set[tuple[str, str]]

Extracts which port-clock combinations are used in this operation.

Returns:

: All (port, clock) combinations this operation uses.

classmethod is_valid(object_to_be_validated: Operation) → bool

Validates the object’s contents against the schema.

Additionally, checks if the hash property of the object evaluates correctly.

property valid_gate: bool

An operation is a valid gate if it has gate-level representation details.

property valid_pulse: bool

An operation is a valid pulse if it has pulse-level representation details.

property valid_acquisition: bool

An operation is a valid acquisition if it has pulse-level acquisition representation details.

property valid_statement: bool

An operation is a valid statement if it has statement-level representation details.

property is_conditional_acquisition: bool

An operation is conditional if one of the following holds, self is an an acquisition with a feedback_trigger_label assigned to it.

property is_control_flow: bool

Determine if operation is a control flow operation.

Returns:

bool Whether the operation is a control flow operation.

property has_voltage_offset: bool

Checks if the operation contains information for a voltage offset.

class PulseCompensation(body: qblox_scheduler.operations.operation.Operation | qblox_scheduler.schedules.schedule.TimeableSchedule | qblox_scheduler.schedule.Schedule, qubits: str | collections.abc.Iterable[str] | None = None, max_compensation_amp: dict[Port, float] | None = None, time_grid: float | None = None, sampling_rate: float | None = None, min_duration: float | None = None)

Bases: qblox_scheduler.operations.operation.Operation

Apply pulse compensation to an operation or schedule.

Inserts a pulse at the end of the operation or schedule set in body for each port. The compensation pulses are calculated so that the integral of all pulses (including the compensation pulses) are zero for each port. Moreover, the compensating pulses are square pulses, and start just after the last pulse on each port individually, and their maximum amplitude is the one specified in the max_compensation_amp. Their duration is divisible by duration_grid. The clock is assumed to be the baseband clock; any other clock is not allowed.

Parameters:

• body – Operation to be pulse-compensated

• qubits – For composite operations, this is a list of device element names.

• max_compensation_amp – Dictionary for each port the maximum allowed amplitude for the compensation pulse.

• time_grid – Grid time of the duration of the compensation pulse.

• sampling_rate – Sampling rate for pulse integration calculation.

• min_duration – The minimum duration of the compensation pulse.

property body: qblox_scheduler.operations.operation.Operation | qblox_scheduler.schedules.schedule.TimeableSchedule

Body of a pulse compensation.

property max_compensation_amp: dict[Port, float]

For each port the maximum allowed amplitude for the compensation pulse.

property time_grid: float

Grid time of the duration of the compensation pulse.

property sampling_rate: float

Sampling rate for pulse integration calculation.

property min_duration: float

The minimum duration of the compensation pulse.

class ChirpPulse(amplitude: float | qblox_scheduler.operations.expressions.Expression | collections.abc.Sequence[float | qblox_scheduler.operations.expressions.Expression], duration: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str, start_freq: float | qblox_scheduler.operations.expressions.Expression, end_freq: float | qblox_scheduler.operations.expressions.Expression, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

A linear chirp signal. A sinusoidal signal that ramps up in frequency.

Parameters:

• amplitude – Unitless amplitude of the envelope.

• duration – Duration of the pulse.

• port – The port of the pulse.

• clock – Clock used to modulate the pulse.

• start_freq – Start frequency of the Chirp. Note that this is the frequency at which the waveform is calculated, this may differ from the clock frequency.

• end_freq – End frequency of the Chirp.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.chirp'

ignored_pulse_params: ClassVar[set[str]]

class DRAGPulse(amplitude: float | qblox_scheduler.operations.expressions.Expression, beta: float | qblox_scheduler.operations.expressions.Expression, duration: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str, sigma: float | qblox_scheduler.operations.expressions.Expression | None = None, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

A Gaussian pulse with a derivative component added to the out-of-phase channel. It uses the specified amplitude and sigma. If sigma is not specified it is set to 1/4 of the duration.

The DRAG pulse is intended for single qubit gates in transmon based systems. It can be calibrated to reduce unwanted excitations of the \(|1\rangle - |2\rangle\) transition ( Motzoi et al., 2009 and Gambetta et al., 2011).

The waveform is generated using waveforms.drag() .

Parameters:

• amplitude – Unitless amplitude of the Gaussian envelope.

• beta – Amplitude of the derivative component, the DRAG-pulse parameter in seconds.

• duration – The pulse duration in seconds.

• phase – Phase of the pulse in degrees.

• clock – Clock used to modulate the pulse.

• port – Port of the pulse, must be capable of carrying a complex waveform.

• sigma – Width of the Gaussian envelope in seconds. If not provided, the sigma is set to 1/4 of the duration.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.drag'

class GaussPulse(amplitude: float | qblox_scheduler.operations.expressions.Expression, duration: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = BasebandClockResource.IDENTITY, sigma: float | qblox_scheduler.operations.expressions.Expression | None = None, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

The GaussPulse Operation is a real-valued pulse with the specified amplitude and sigma. If sigma is not specified it is set to 1/4 of the duration.

The waveform is generated using waveforms.drag() with a beta set to zero, corresponding to a Gaussian pulse.

Parameters:

• amplitude – Unitless amplitude of the Gaussian envelope.

• duration – The pulse duration in seconds.

• phase – Phase of the pulse in degrees.

• clock – Clock used to modulate the pulse. By default the baseband clock.

• port – Port of the pulse, must be capable of carrying a complex waveform.

• sigma – Width of the Gaussian envelope in seconds. If not provided, the sigma is set to 1/4 of the duration.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.drag'

class IdlePulse(duration: float | qblox_scheduler.operations.expressions.Expression)

Bases: _PulseOperationBase

The IdlePulse Operation is a placeholder for a specified duration of time.

Parameters:

duration – The duration of idle time in seconds.

class MarkerPulse(duration: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = DigitalClockResource.IDENTITY, fine_start_delay: float | qblox_scheduler.operations.expressions.Expression = 0.0, fine_end_delay: float | qblox_scheduler.operations.expressions.Expression = 0.0)

Bases: _PulseOperationBase

Digital pulse that is HIGH for the specified duration.

Marker pulse is played on marker output. Currently only implemented for Qblox backend.

Parameters:

• duration – Duration of the HIGH signal.

• port – Name of the associated port.

• clock – Name of the associated clock. By default DigitalClockResource. This only needs to be specified if a custom clock name is used for a digital channel (for example, when a port-clock combination of a device element is used with a digital channel).

• fine_start_delay – Delays the start of the pulse by the given amount in seconds. Does not delay the start time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the pulse by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

• fine_end_delay – Delays the end of the pulse by the given amount in seconds. Does not delay the end time of the operation in the schedule. If the hardware supports it, this parameter can be used to shift the pulse by a small amount of time, independent of the hardware instruction timing grid. Currently only implemented for Qblox QTM modules, which allow only positive values for this parameter. By default 0.

class NumericalPulse(samples: numpy.ndarray | list, t_samples: numpy.ndarray | list, port: str, clock: str = BasebandClockResource.IDENTITY, amplitude: complex | float | qblox_scheduler.operations.expressions.Expression | collections.abc.Sequence[complex | float | qblox_scheduler.operations.expressions.Expression] = 1.0, interpolation: str = 'linear', **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

A pulse where the shape is determined by specifying an array of (complex) points.

If points are required between the specified samples (such as could be required by the sampling rate of the hardware), meaning \(t[n] < t' < t[n+1]\), scipy.interpolate.interp1d will be used to interpolate between the two points and determine the value.

Parameters:

• samples – An array of (possibly complex) values specifying the shape of the pulse.

• t_samples – An array of values specifying the corresponding times at which the samples are evaluated.

• port – The port that the pulse should be played on.

• clock – Clock used to (de)modulate the pulse. By default the baseband clock.

• amplitude – Gain factor between -1 and 1 that multiplies with the samples, by default 1.

• interpolation – Specifies the type of interpolation used. This is passed as the “kind” argument to scipy.interpolate.interp1d.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.interpolated_complex_waveform'

class RampPulse(amplitude: float | qblox_scheduler.operations.expressions.Expression, duration: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = BasebandClockResource.IDENTITY, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

RampPulse Operation is a pulse that ramps from zero to a set amplitude over its duration.

The pulse is given as a function of time \(t\) and the parameters offset and amplitude by

\[P(t) = \mathrm{voltage_offset} + t \times \frac{\mathrm{amplitude}}{\mathrm{duration}}\]

Parameters:

• amplitude – Unitless amplitude of the ramp envelope function.

• duration – The pulse duration in seconds.

• port – Port of the pulse.

• clock – Clock used to modulate the pulse. By default the baseband clock.

• **signal_args – Additional signal parameters defined in PulseSignalArgs. The only relevant argument for the ramppulse is voltage_offset.

wf_func = 'qblox_scheduler.waveforms.ramp'

class ResetClockPhase(clock: str)

Bases: _PulseOperationBase

An operation that resets the phase of the NCO clock. Bear in mind, that this might not reset the full phase, but only just the phase of one component of the frequency.

Parameters:

clock – The clock of which to reset the phase.

class SetClockFrequency(clock: str, frequency: float | qblox_scheduler.operations.expressions.Expression | None)

Bases: _PulseOperationBase

Operation that sets updates the frequency of a clock.

This is a low-level operation and therefore depends on the backend.

Currently only implemented for Qblox backend, refer to NcoSetClockFrequencyStrategy for more details.

Parameters:

• clock – The clock for which a new frequency is to be set.

• frequency – The new frequency in Hz. If None, it will reset to the clock frequency set by the configuration or resource.

class ShiftClockPhase(phase_shift: float | qblox_scheduler.operations.expressions.Expression, clock: str)

Bases: _PulseOperationBase

Operation that shifts the phase of a clock by a specified amount.

This is a low-level operation and therefore depends on the backend.

Currently only implemented for Qblox backend, refer to NcoPhaseShiftStrategy for more details.

Parameters:

• phase_shift – The phase shift in degrees.

• clock – The clock of which to shift the phase.

class SkewedHermitePulse(duration: float | qblox_scheduler.operations.expressions.Expression, amplitude: float | qblox_scheduler.operations.expressions.Expression, skewness: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

Hermite pulse intended for single qubit gates in diamond based systems.

The waveform is generated using skewed_hermite().

Parameters:

• duration – The pulse duration in seconds.

• amplitude – Unitless amplitude of the hermite pulse.

• skewness – Skewness in the frequency space.

• phase – Phase of the pulse in degrees.

• clock – Clock used to modulate the pulse.

• port – Port of the pulse, must be capable of carrying a complex waveform.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.skewed_hermite'

class SoftSquarePulse(amplitude: float | qblox_scheduler.operations.expressions.Expression | collections.abc.Sequence[float | qblox_scheduler.operations.expressions.Expression], duration: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = BasebandClockResource.IDENTITY, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

A real valued square pulse convolved with a Hann window for smoothing.

Parameters:

• amplitude – Unitless amplitude of the envelope.

• duration – The pulse duration in seconds.

• port – Port of the pulse, must be capable of playing a complex waveform.

• clock – Clock used to modulate the pulse. By default the baseband clock.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.soft_square'

class SquarePulse(amplitude: complex | float | qblox_scheduler.operations.expressions.Expression | collections.abc.Sequence[complex | float | qblox_scheduler.operations.expressions.Expression], duration: complex | float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = BasebandClockResource.IDENTITY, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

A real-valued pulse with the specified amplitude during the pulse.

Parameters:

• amplitude – Unitless complex valued amplitude of the envelope.

• duration – The pulse duration in seconds.

• port – Port of the pulse, must be capable of playing a complex waveform.

• clock – Clock used to modulate the pulse. By default the baseband clock.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.square'

class StaircasePulse(start_amp: float | qblox_scheduler.operations.expressions.Expression, final_amp: float | qblox_scheduler.operations.expressions.Expression, num_steps: int, duration: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = BasebandClockResource.IDENTITY)

Bases: _PulseOperationBase

A real valued staircase pulse, which reaches it’s final amplitude in discrete steps.

In between it will maintain a plateau.

Parameters:

• start_amp – Starting unitless amplitude of the staircase envelope function.

• final_amp – Final unitless amplitude of the staircase envelope function.

• num_steps – The number of plateaus.

• duration – Duration of the pulse in seconds.

• port – Port of the pulse.

• clock – Clock used to modulate the pulse. By default the baseband clock.

wf_func = 'qblox_scheduler.waveforms.staircase'

class SuddenNetZeroPulse(amp_A: float | qblox_scheduler.operations.expressions.Expression, amp_B: float | qblox_scheduler.operations.expressions.Expression, net_zero_A_scale: float | qblox_scheduler.operations.expressions.Expression, t_pulse: float | qblox_scheduler.operations.expressions.Expression, t_phi: float | qblox_scheduler.operations.expressions.Expression, t_integral_correction: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = BasebandClockResource.IDENTITY, **signal_args: Unpack[PulseSignalArgs])

Bases: _PulseOperationBase

A pulse that can be used to implement a conditional phase gate in transmon device elements.

The sudden net-zero (SNZ) pulse is defined in Negirneac et al., 2021.

Parameters:

• amp_A – Unitless amplitude of the main square pulse.

• amp_B – Unitless scaling correction for the final sample of the first square and first sample of the second square pulse.

• net_zero_A_scale – Amplitude scaling correction factor of the negative arm of the net-zero pulse.

• t_pulse – The total duration of the two half square pulses

• t_phi – The idling duration between the two half pulses

• t_integral_correction – The duration in which any non-zero pulse amplitude needs to be corrected.

• port – Port of the pulse, must be capable of playing a complex waveform.

• clock – Clock used to modulate the pulse. By default the baseband clock.

• **signal_args – Additional signal parameters defined in PulseSignalArgs.

wf_func = 'qblox_scheduler.waveforms.sudden_net_zero'

ignored_pulse_params: ClassVar[set[str]]

class Timestamp(port: str, clock: str = DigitalClockResource.IDENTITY)

Bases: _PulseOperationBase

Operation that marks a time reference for timetags.

Specifically, all timetags in Timetag and TimetagTrace are measured relative to the timing of this operation, if they have a matching port and clock, and if time_ref=TimeRef.TIMESTAMP is given as an argument.

Parameters:

• port – The same port that the timetag acquisition is defined on.

• clock – The same clock that the timetag acquisition is defined on.

class VoltageOffset(offset_path_I: float | qblox_scheduler.operations.expressions.Expression, offset_path_Q: float | qblox_scheduler.operations.expressions.Expression, port: str, clock: str = BasebandClockResource.IDENTITY)

Bases: _PulseOperationBase

Operation that represents setting a constant offset to the output voltage.

Please refer to sec-qblox-offsets-long-voltage-offsets in the reference guide for more details.

Parameters:

• offset_path_I (float) – Offset of path I.

• offset_path_Q (float) – Offset of path Q.

• port (str) – Port of the voltage offset.

• clock (str, Optional) – Clock used to modulate the voltage offset. By default the baseband clock.

class HardwareAgent(hardware_configuration: qblox_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig | dict | str | pathlib.Path, quantum_device_configuration: dict | str | pathlib.Path | qblox_scheduler.device_under_test.quantum_device.QuantumDevice | None = None, *, debug: qblox_instruments.types.DebugLevel = DebugLevel.MINIMAL_CHECK, output_dir: pathlib.Path | str | None = None, recreate_instruments: bool = True, create_dummy_connections: bool = False)

Hardware definition of qblox backend. Contains definitions for the cluster, the hardware configuration, and related classes to manage the instruments.

Parameters:

• hardware_configuration –

  The hardware configuration. Either - a QbloxHardwareCompilationConfig instance - a dictionary as pydantic model of the QbloxHardwareCompilationConfig - a json file that stores the above dictionary

  The cluster fields in “hardware_description” will have its modules field autogenerated, based on its ip field when they are not defined. This ip field is mandatory.

• quantum_device_configuration – The quantum device configuration. Either - a json dictionary of the QuantumDevice - a path to the file that stores the json dictionary

• debug – Debug level of the cluster. By default, it is set to DebugLevel.MINIMAL_CHECK. Available debug levels are: - DebugLevel.MINIMAL_CHECK: Check compatibility between the hardware firmware version and qblox-instruments version, and check for errors when starting and stopping sequencers. - DebugLevel.ERROR_CHECK: Check for errors after every low-level cluster command. - DebugLevel.NO_CHECK: Do not check for errors. - DebugLevel.VERSION_AND_ERROR_CHECK: Combination of DebugLEVEL.MINIMAL_CHECK and DebugLevel.ERROR_CHECK.

• output_dir – Output directory where the data will be stored.

• recreate_instruments – Whether to recreate the existing instruments when the cluster is reinstantiated. By default, it is set to True.

• create_dummy_connections – Create clusters with dummy configurations, mocking the connection to a physical instrument.

_ElementType

_num_instances_initiated = 0

_hardware_configuration

_clusters: dict[str, qblox_scheduler.instrument_coordinator.components.qblox.ClusterComponent]

_noncluster_instruments: dict[str, qcodes.instrument.instrument.Instrument]

_instrument_coordinator

_debug

_recreate_instruments = True

_create_dummy_connections = False

_latest_dataset: xarray.Dataset | None = None

_latest_schedule: qblox_scheduler.schedule.Schedule | qblox_scheduler.schedules.schedule.TimeableSchedule | None = None

_latest_compiled_schedule: qblox_scheduler.schedules.schedule.CompiledSchedule | None = None

property hardware_configuration: qblox_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig

The (validated) hardware configuration of this hardware. Only available after the connections with the clusters have been established using connect_clusters if initially given as a file/dictionary. Note that this is not in json/dictionary format.

add_device_elements(device_elements: collections.abc.Sequence[qblox_scheduler.device_under_test.device_element.DeviceElement]) → None

Add multiple device elements to the quantum device.

Parameters:

device_elements – list of device elements to be added

add_edges(edges: list[qblox_scheduler.device_under_test.edge.Edge]) → None

Add multiple edges to the quantum device.

Parameters:

edges – list of edges to be added

static _record_module_isa_version(module: dict | object, module_instrument: object) → None

Fill in the module description’s sequencer ISA version from the instrument.

connect_clusters() → None

Connect the defined clusters to the hardware given their identifiers.

Will also supplement the modules to the given hardware description and/or verify them.

property quantum_device: qblox_scheduler.device_under_test.quantum_device.QuantumDevice

The quantum device active in this hardware.

The quantum device contains the device elements and how the connectivity between them is defined.

property instrument_coordinator: qblox_scheduler.instrument_coordinator.instrument_coordinator.InstrumentCoordinator

The instrument coordinator active in this hardware.

The instrument coordinator is responsible for executing the (compiled) schedule on the instruments in this backend.

get_clusters() → dict[str, qblox_instruments.Cluster]

Get all the instantiated clusters.

Returns:

A dictionary mapping cluster names to Cluster objects.

property latest_compiled_schedule: qblox_scheduler.schedules.schedule.CompiledSchedule | None

Get the latest compiled schedule, if one exists.

run(schedule: qblox_scheduler.schedule.Schedule | qblox_scheduler.schedules.schedule.TimeableSchedule | qblox_scheduler.schedules.schedule.CompiledSchedule, *, segment_repetitions: int | None = None, timeout: int = 10, save_to_experiment: bool = True, save_snapshot: bool = True) → xarray.Dataset

Run a schedule on the hardware.

Parameters:

• schedule – The schedule to run.

• segment_repetitions – Repeat the execution of each segment this many times.

• timeout – The timeout for retrieving the results, in seconds

• save_to_experiment – Whether to save the dataset to an experiment directory. The dataset and (optionally) snapshot will be saved in <datadir>/<tuid>/dataset.hdf5 and <datadir>/<tuid>/snapshot.json, where datadir is specified by set_datadir() and get_datadir()

• save_snapshot – Whether to save a snapshot of the experiment

Returns:

Acquisition result dataset if any acquisitions are used in the schedule. Empty dataset otherwise.

Raises:

TimeoutError – When hardware doesn’t return results in the given timeout.

static _change_acq_protocol_attribute(dataset: xarray.Dataset) → None

Changing the acquisition protocol in the returned dataset’s attributes.

The attributes are used currently by the analysis classes (see quantify_utils._process_acquired_data and reshape_for_analysis), so we cannot yet remove it. We cannot change the deprecated protocol names to IQIntegration, because we distinguish the SSBIntegrationComplex and NumericalSeparatedWeightedIntegration in the compiler. So we simply rename these deprecated protocols to IQIntegration.

Todo: this needs to be modified so that internally we use the IQIntegration too.

compile(schedule: qblox_scheduler.schedule.Schedule | qblox_scheduler.schedules.schedule.TimeableSchedule) → qblox_scheduler.schedules.schedule.CompiledSchedule | list[qblox_scheduler.segments.segment.Segment | qblox_scheduler.schedules.schedule.CompiledSchedule]

Compile the schedule to the hardware. Compilation is already done in the run method, so run this method only if the compiled schedule requires to be investigated.

Parameters:

schedule – The TimeableSchedule to compile for this hardware

Returns:

The compiled schedule

static set_output_data_dir(datadir: pathlib.Path | str | None = None) → None

Sets the output data directory.

Parameters:

datadir – Path of the data directory. If set to None, resets the datadir to the default datadir (<top_level>/data).

create_debug_log(output_directory: pathlib.Path | str | None = '') → pathlib.Path

Create a zip file with debug information.

The zip file includes: - device configuration - hardware configuration - compiled schedule - instrument snapshot - hardware logs

Parameters:

output_directory – Directory where the log file will be saved. By default the directory set with HardwareAgent.set_output_data_dir.

Returns:

Absolute path to the created log file.

_verify_hardware_configuration() → None

Verifies if the given hardware configuration can be run on the cluster.

Raises:

• TypeError – When the hardware_configuration has not been correctly initialized yet.

• ValueError – When the hardware configuration does not match the modules installed on the cluster.

_create_cluster(cluster_name: str, cluster_description: dict | qblox_scheduler.backends.types.qblox.ClusterDescription) → qblox_instruments.Cluster

_create_dummy_cluster(cluster_name: str, cluster_description: dict | qblox_scheduler.backends.types.qblox.ClusterDescription) → qblox_instruments.Cluster

class BasebandClockResource(name: str)

Bases: Resource

Global identity for a virtual baseband clock.

Baseband signals are assumed to be real-valued and will not be modulated.

Parameters:

name – the name of this clock

IDENTITY = 'cl0.baseband'

data

class ClockResource(name: str, freq: float, phase: float = 0)

Bases: Resource

The ClockResource corresponds to a physical clock used to modulate pulses.

Parameters:

• name – the name of this clock

• freq – the frequency of the clock in Hz

• phase – the starting phase of the clock in deg

data

class DigitalClockResource(name: str)

Bases: Resource

Global identity for a virtual digital clock.

Digital clocks can only be associated with digital channels.

Parameters:

name – the name of this clock

IDENTITY = 'digital'

data

class Schedule(name: str = 'schedule', repetitions: int = 1)

High-level hybrid schedule.

_experiments

_resources = []

_schedule_count = None

_repetitions = 1

_uses_timeable_repetitions = False

_duration = None

_loop_variables: set[qblox_scheduler.operations.variables.Variable]

clone() → Schedule

Clone this schedule into a separate independent schedule.

substitute(substitutions: dict[qblox_scheduler.operations.expressions.Expression, qblox_scheduler.operations.expressions.Expression | int | float | complex]) → Schedule

Substitute matching expressions of operations in this schedule.

_set_repetitions(value: int) → None

property _experiment: qblox_scheduler.segments.segment.Experiment

Returns the current experiment.

property name: str

Returns the name of the schedule.

property _timeable_schedules: list[qblox_scheduler.schedules.schedule.TimeableScheduleBase]

Returns a list of timeable schedules in this schedule.

property _last_timeable_schedule: qblox_scheduler.schedules.schedule.TimeableSchedule | None

Returns the last timeable schedule in this schedule.

property _last_compiled_timeable_schedule: qblox_scheduler.schedules.schedule.CompiledSchedule | None

Returns the last compiled timeable schedule in this schedule.

property _timeable_schedule: qblox_scheduler.schedules.schedule.TimeableScheduleBase | None

Returns the single timeable schedule in this schedule, or None.

get_schedule_duration() → float

Return total duration of all timeable schedules.

Returns:

schedule_duration : float Duration of current schedule

property duration: float | None

Determine the cached duration of the schedule.

Will return None if get_schedule_duration() has not been called before.

property data: dict

The dictionary data of a single contained timeable schedule.

property operations: dict[str, qblox_scheduler.operations.operation.Operation | qblox_scheduler.schedules.schedule.TimeableSchedule]

A dictionary of all unique operations used in the schedule.

This specifies information on what operation to apply where.

The keys correspond to the hash and values are instances of qblox_scheduler.operations.operation.Operation.

property schedulables: dict[str, qblox_scheduler.schedules.schedule.Schedulable]

Ordered dictionary of schedulables describing timing and order of operations.

A schedulable uses timing constraints to constrain the operation in time by specifying the time ("rel_time") between a reference operation and the added operation. The time can be specified with respect to a reference point ("ref_pt"') on the reference operation (:code:”ref_op”) and a reference point on the next added operation (:code:”ref_pt_new”’). A reference point can be either the “start”, “center”, or “end” of an operation. The reference operation ("ref_op") is specified using its label property.

Each item in the list represents a timing constraint and is a dictionary with the following keys:

['label', 'rel_time', 'ref_op', 'ref_pt_new', 'ref_pt', 'operation_id']

The label is used as a unique identifier that can be used as a reference for other operations, the operation_id refers to the hash of an operation in operations.

Note

timing constraints are not intended to be modified directly. Instead use the add()

plot_composite_diagram(figsize: tuple[int, int] | None = None, ax: matplotlib.axes.Axes | None = None, plot_backend: Literal['mpl'] = 'mpl', timeable_schedule_index: int | None = None) → tuple[matplotlib.figure.Figure | None, matplotlib.axes.Axes | list[matplotlib.axes.Axes]]

Create a circuit diagram visualization of the schedule using the specified plotting backend.

The circuit diagram visualization depicts the schedule at the quantum circuit layer. Because qblox-scheduler uses a hybrid gate-pulse paradigm, operations for which no information is specified at the gate level are visualized using an icon (e.g., a stylized wavy pulse) depending on the information specified at the quantum device layer.

Alias of qblox_scheduler.schedules._visualization.circuit_diagram.circuit_diagram_matplotlib().

Parameters:

• figsize – matplotlib figsize.

• ax – Axis handle to use for plotting.

• plot_backend – Plotting backend to use, currently only ‘mpl’ is supported

• timeable_schedule_index – Index of timeable schedule in schedule to plot. If None (the default), will only plot if the schedule contains a single timeable schedule.

Returns:

fig

matplotlib figure object.

ax

matplotlib axis object.

Each gate, pulse, measurement, and any other operation are plotted in the order of execution, but no timing information is provided.

Example

from qblox_scheduler import Schedule
from qblox_scheduler import Reset, X90, CZ, Rxy, Measure

sched = Schedule(f"Bell experiment on q0-q1")

sched.add(Reset("q0", "q1"))
sched.add(X90("q0"))
sched.add(X90("q1"), ref_pt="start", rel_time=0)
sched.add(CZ(qC="q0", qT="q1"))
sched.add(Rxy(theta=45, phi=0, qubit="q0") )
sched.add(Measure("q0", acq_index=0))
sched.add(Measure("q1", acq_index=0), ref_pt="start")

sched.plot_composite_diagram()

(<Figure size 1000x200 with 1 Axes>,
 <Axes: title={'center': 'Bell experiment on q0-q1 schedule 1'}>)

Note

Gates that are started simultaneously on the same qubit will overlap.

from qblox_scheduler import Schedule
from qblox_scheduler import X90, Measure

sched = Schedule(f"overlapping gates")

sched.add(X90("q0"))
sched.add(Measure("q0"), ref_pt="start", rel_time=0)
sched.plot_composite_diagram();

Note

If the pulse’s port address was not found then the pulse will be plotted on the ‘other’ timeline.

plot_circuit_diagram(*args, **kwargs) → tuple[matplotlib.figure.Figure | None, matplotlib.axes.Axes | list[matplotlib.axes.Axes]]

plot_pulse_diagram(port_list: list[str] | None = None, sampling_rate: float = 1000000000.0, modulation: Literal['off', 'if', 'clock'] = 'off', modulation_if: float = 0.0, plot_backend: Literal['mpl', 'plotly'] = 'mpl', x_range: tuple[float, float] = (-np.inf, np.inf), combine_waveforms_on_same_port: bool = True, timeable_schedule_index: int | None = None, num_repetitions: int | None = 1, **backend_kwargs) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes] | plotly.graph_objects.Figure

Create a visualization of all the pulses in a schedule using the specified plotting backend.

The pulse diagram visualizes the schedule at the quantum device layer. For this visualization to work, all operations need to have the information present (e.g., pulse info) to represent these on the quantum-circuit level and requires the absolute timing to have been determined. This information is typically added when the quantum-device level compilation is performed.

Alias of qblox_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_matplotlib() and qblox_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_plotly().

Parameters:

• port_list – A list of ports to show. If None (default) the first 8 ports encountered in the sequence are used.

• modulation – Determines if modulation is included in the visualization.

• modulation_if – Modulation frequency used when modulation is set to “if”.

• sampling_rate – The time resolution used to sample the schedule in Hz.

• plot_backend – Plotting library to use, can either be ‘mpl’ or ‘plotly’.

• x_range – The range of the x-axis that is plotted, given as a tuple (left limit, right limit). This can be used to reduce memory usage when plotting a small section of a long pulse sequence. By default (-np.inf, np.inf).

• combine_waveforms_on_same_port – By default True. If True, combines all waveforms on the same port into one single waveform. The resulting waveform is the sum of all waveforms on that port (small inaccuracies may occur due to floating point approximation). If False, the waveforms are shown individually.

• timeable_schedule_index – Index of timeable schedule in schedule to plot. If None (the default), will only plot if the schedule contains a single timeable schedule.

• num_repetitions – The number of times the schedule’s top-level repetitions are expanded in the plot. By default 1, i.e. only the first repetition is shown. Set to None to expand all repetitions. Note this only affects the loop synthesized from the schedule’s top-level repetitions; any other loop/sweep in the schedule is always fully expanded.

• backend_kwargs – Keyword arguments to be passed on to the plotting backend. The arguments that can be used for either backend can be found in the documentation of qblox_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_matplotlib() and qblox_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_plotly().

Returns:

Union[tuple[Figure, Axes], plotly.graph_objects.Figure] the plot

Example

A simple plot with matplotlib can be created as follows:

from qblox_scheduler.backends.graph_compilation import SerialCompiler
from qblox_scheduler import QuantumDevice
from qblox_scheduler import (
    DRAGPulse, SquarePulse, RampPulse, VoltageOffset,
)
from qblox_scheduler import ClockResource

schedule = Schedule("Multiple waveforms")
schedule.add(DRAGPulse(amplitude=0.2, beta=2e-9, phase=0, duration=4e-6, port="P", clock="C"))
schedule.add(RampPulse(amplitude=0.2, voltage_offset=0.0, duration=6e-6, port="P"))
schedule.add(SquarePulse(amplitude=0.1, duration=4e-6, port="Q"), ref_pt='start')
schedule.add_resource(ClockResource(name="C", freq=4e9))

quantum_device = QuantumDevice("quantum_device")
device_compiler = SerialCompiler("Device compiler", quantum_device)
compiled_schedule = device_compiler.compile(schedule)

_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6)

The backend can be changed to the plotly backend by specifying the plot_backend=plotly argument. With the plotly backend, pulse diagrams include a separate plot for each port/clock combination:

_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6, plot_backend='plotly')

The same can be achieved in the default plot_backend (matplotlib) by passing the keyword argument multiple_subplots=True:

_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6, multiple_subplots=True)

By default, waveforms overlapping in time on the same port are shown separately:

schedule = Schedule("Overlapping waveforms")
schedule.add(VoltageOffset(offset_path_I=0.25, offset_path_Q=0.0, port="Q"))
schedule.add(SquarePulse(amplitude=0.1, duration=4e-6, port="Q"), rel_time=2e-6)
schedule.add(VoltageOffset(offset_path_I=0.0, offset_path_Q=0.0, port="Q"), ref_pt="start", rel_time=2e-6)

compiled_schedule = device_compiler.compile(schedule)

_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6)

This behaviour can be changed with the parameter combine_waveforms_on_same_port:

_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6, combine_waveforms_on_same_port=True)

property timing_table: pandas.io.formats.style.Styler

A styled pandas dataframe containing the absolute timing of pulses and acquisitions in a schedule.

This table is constructed based on the abs_time key in the schedulables. This requires the timing to have been determined.

The table consists of the following columns:

• operation: a repr of Operation corresponding to the pulse/acquisition.

• waveform_op_id: an id corresponding to each pulse/acquisition inside an Operation.

• port: the port the pulse/acquisition is to be played/acquired on.

• clock: the clock used to (de)modulate the pulse/acquisition.

• abs_time: the absolute time the pulse/acquisition is scheduled to start.

• duration: the duration of the pulse/acquisition that is scheduled.

• is_acquisition: whether the pulse/acquisition is an acquisition or not (type numpy.bool_).

• wf_idx: the waveform index of the pulse/acquisition belonging to the Operation.

• operation_hash: the unique hash corresponding to the Schedulable that the pulse/acquisition belongs to.

Example

schedule = Schedule("demo timing table")
schedule.add(Reset("q0", "q4"))
schedule.add(X("q0"))
schedule.add(Y("q4"))
schedule.add(Measure("q0", acq_label=0))

compiled_schedule = compiler.compile(schedule)
compiled_schedule.timing_table

	waveform_op_id	port	clock	abs_time	duration	is_acquisition	operation	operation_hash
0	ResetClockPhase(clock='q0.ro')	None	q0.ro	0.0 ns	0.0 ns	False	ResetClockPhase(clock='q0.ro')	4409040959015671762
1	ResetClockPhase(clock='q4.01')	None	q4.01	0.0 ns	0.0 ns	False	ResetClockPhase(clock='q4.01')	4879054775067014605
2	ResetClockPhase(clock='q0.01')	None	q0.01	0.0 ns	0.0 ns	False	ResetClockPhase(clock='q0.01')	5556117654481544042
3	IdlePulse(duration=4e-09)	None	cl0.baseband	0.0 ns	4.0 ns	False	IdlePulse(duration=4e-09)	2467483450922559885
4	IdlePulse(duration=0.0002)	None	cl0.baseband	4.0 ns	200,000.0 ns	False	IdlePulse(duration=0.0002)	9062590787514744152
5	IdlePulse(duration=0.0002)	None	cl0.baseband	4.0 ns	200,000.0 ns	False	IdlePulse(duration=0.0002)	9062590787514744152
6	X(qubit='q0')	q0:mw	q0.01	200,004.0 ns	20.0 ns	False	X(qubit='q0')	6164511457188911909
7	Y(qubit='q4')	q4:mw	q4.01	200,024.0 ns	20.0 ns	False	Y(qubit='q4')	7202329202446696529
8	ResetClockPhase(clock='q0.ro')	None	q0.ro	200,044.0 ns	0.0 ns	False	ResetClockPhase(clock='q0.ro')	4409040959015671762
9	VoltageOffset(offset_path_I=0.25,offset_path_Q=0,port='q0:res',clock='q0.ro')	q0:res	q0.ro	200,044.0 ns	0.0 ns	False	VoltageOffset(offset_path_I=0.25,offset_path_Q=0,port='q0:res',clock='q0.ro')	-7134753899540842834
12	IQIntegration(port='q0:res', clock='q0.ro', duration=1e-06, acq_label=0, coords=None, average_segment_repetitions=True)	q0:res	q0.ro	200,144.0 ns	1,000.0 ns	True	IQIntegration(port='q0:res', clock='q0.ro', duration=1e-06, acq_label=0, coords=None, average_segment_repetitions=True)	-1702236331988111396
10	VoltageOffset(offset_path_I=0.0,offset_path_Q=0.0,port='q0:res',clock='q0.ro')	q0:res	q0.ro	200,340.0 ns	0.0 ns	False	VoltageOffset(offset_path_I=0.0,offset_path_Q=0.0,port='q0:res',clock='q0.ro')	5047209966467110297
11	SquarePulse(amplitude=0.25,duration=4e-09,port='q0:res',clock='q0.ro')	q0:res	q0.ro	200,340.0 ns	4.0 ns	False	SquarePulse(amplitude=0.25,duration=4e-09,port='q0:res',clock='q0.ro')	5320609911628649773

Note that only the first repetition of the schedule’s top-level repetitions is shown; any other loop/sweep in the schedule is fully expanded.

Returns:

: styled_timing_table, a pandas Styler containing a dataframe with an overview of the timing of the pulses and acquisitions present in the schedule. The dataframe can be accessed through the .data attribute of the Styler.

Raises:

ValueError – When the absolute timing has not been determined during compilation.

_add_timeable_schedule(timeable_schedule: qblox_scheduler.schedules.schedule.TimeableSchedule) → None

_get_current_timeable_schedule() → qblox_scheduler.schedules.schedule.TimeableSchedule | None

_get_timeable_schedule() → qblox_scheduler.schedules.schedule.TimeableSchedule

get_used_port_clocks() → set[tuple[str, str]]

Extracts which port-clock combinations are used in this schedule.

Returns:

: All (port, clock) combinations that operations in this schedule uses

add_resources(resources_list: list) → None

Add wrapper for adding multiple resources.

add_resource(resource: qblox_scheduler.resources.Resource) → None

Add a resource such as a label or device element to the schedule.

declare(dtype: qblox_scheduler.operations.expressions.DType) → qblox_scheduler.operations.variables.Variable

Declare a new variable.

Parameters:

dtype – The data type of the variable.

static _merge_previous_schedules(experiment: qblox_scheduler.segments.segment.Experiment) → None

add(operation: Schedule | qblox_scheduler.operations.operation.Operation | qblox_scheduler.schedules.schedule.TimeableSchedule | qblox_scheduler.segments.segment.Segment, rel_time: float | qblox_scheduler.operations.expressions.Expression | None = 0, ref_op: qblox_scheduler.schedules.schedule.Schedulable | str | None = None, ref_pt: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, ref_pt_new: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, label: str | None = None) → qblox_scheduler.schedules.schedule.Schedulable | None

Add segment, operation or timeable schedule to this schedule.

_add_schedule(schedule: Schedule, rel_time: float | qblox_scheduler.operations.expressions.Expression | None = 0, ref_op: qblox_scheduler.schedules.schedule.Schedulable | str | None = None, ref_pt: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, ref_pt_new: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, label: str | None = None) → qblox_scheduler.schedules.schedule.Schedulable | None

loop(domain: qblox_scheduler.operations.loop_domains.LinearDomain, *, rel_time: float | None = 0, ref_op: qblox_scheduler.schedules.schedule.Schedulable | str | None = None, ref_pt: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, ref_pt_new: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, strategy: qblox_scheduler.operations.control_flow_library.LoopStrategy | None = None) → contextlib.AbstractContextManager[qblox_scheduler.operations.variables.Variable]

loop(domain: qblox_scheduler.operations.loop_domains.LinearDomain, domains: qblox_scheduler.operations.loop_domains.LinearDomain, /, *args: qblox_scheduler.operations.loop_domains.LinearDomain, rel_time: float | None = 0, ref_op: qblox_scheduler.schedules.schedule.Schedulable | str | None = None, ref_pt: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, ref_pt_new: qblox_scheduler.schedules.schedule.OperationReferencePoint | None = None, strategy: qblox_scheduler.operations.control_flow_library.LoopStrategy | None = None) → contextlib.AbstractContextManager[list[qblox_scheduler.operations.variables.Variable]]

Add a loop operation to the schedule, using a with-statement.

Every operation added while the context manager is active, will be added to the loop body.

Example:

sched = Schedule()
with sched.loop(linspace(start_amp, start_amp + 1.0, 11, dtype=DType.AMP)) as amp:
    sched.add(SquarePulse(amplitude=amp, duration=100e-9, port="q0:mw", clock="q0.01"))

Parameters:

• domain – The object that describes the domain to be looped over.

• domains – Optional extra domains that will be looped over in parallel, in a zip-like fashion.

• rel_time – relative time between the reference operation and the added operation. the time is the time between the “ref_pt” in the reference operation and “ref_pt_new” of the operation that is added. If None, the schedule can be broken up into multiple segments.

• ref_op – reference schedulable. If set to None, will default to the last added operation.

• ref_pt – reference point in reference operation must be one of "start", "center", "end", or None; in case of None, _determine_absolute_timing() assumes "end".

• ref_pt_new – reference point in added operation must be one of "start", "center", "end", or None; in case of None, _determine_absolute_timing() assumes "start".

• strategy – Strategy to use for implementing this loop, will default to None indicating no preference.

Yields:

variables – The Variable objects that are created for each domain.

repeat(n: int) → collections.abc.Iterator[None]

Add a loop operation to the schedule for a given amount of iterations, using a with-statement.

Example:

sched = Schedule()
with sched.repeat(5):
    sched.add(SquarePulse(amplitude=some_amp, duration=100e-9, port="q0:mw", clock="q0.01"))

Parameters:

n – The amount of times to repeat the loop body.

_unrolled_segments() → list[qblox_scheduler.schedules.schedule.TimeableScheduleBase | qblox_scheduler.segments.segment.Segment]

class CompiledSchedule(schedule: TimeableSchedule)

Bases: TimeableScheduleBase

A schedule that contains compiled instructions ready for execution using the InstrumentCoordinator.

The CompiledSchedule differs from a TimeableSchedule in that it is considered immutable (no new operations or resources can be added), and that it contains compiled_instructions.

Tip

A TimeableSchedule can be obtained by compiling a TimeableSchedule using compile().

schema_filename = 'schedule.json'

_hardware_timing_table: pandas.DataFrame

_hardware_waveform_dict: dict[str, numpy.ndarray]

property compiled_instructions: collections.abc.MutableMapping[str, qblox_scheduler.resources.Resource]

A dictionary containing compiled instructions.

The contents of this dictionary depend on the backend it was compiled for. However, we assume that the general format consists of a dictionary in which the keys are instrument names corresponding to components added to a InstrumentCoordinator, and the values are the instructions for that component.

These values typically contain a combination of sequence files, waveform definitions, and parameters to configure on the instrument.

classmethod is_valid(object_to_be_validated: Any) → bool

Check if the contents of the object_to_be_validated are valid.

Additionally checks if the object_to_be_validated is an instance of CompiledSchedule.

property hardware_timing_table: pandas.io.formats.style.Styler

Return a timing table representing all operations at the Control-hardware layer.

Note that this timing table is typically different from the .timing_table in that it contains more hardware specific information such as channels, clock cycles and samples and corrections for things such as gain.

This hardware timing table is intended to provide a more

This table is constructed based on the timing_table and modified during compilation in one of the hardware back ends and optionally added to the schedule. Not all back ends support this feature.

property hardware_waveform_dict: dict[str, numpy.ndarray]

Return a waveform dictionary representing all waveforms at the Control-hardware layer.

Where the waveforms are represented as abstract waveforms in the Operations, this dictionary contains the numerical arrays that are uploaded to the hardware.

This dictionary is constructed during compilation in the hardware back ends and

optionally added to the schedule. Not all back ends support this feature.

clone() → CompiledSchedule

Clone this schedule into a separate independent schedule.

substitute(substitutions: dict[qblox_scheduler.operations.expressions.Expression, qblox_scheduler.operations.expressions.Expression | int | float | complex]) → CompiledSchedule

Substitute matching expressions of operations in this schedule.

class Loop(domains: dict[qblox_scheduler.operations.variables.Variable, qblox_scheduler.operations.loop_domains.LinearDomain], segments: list[qblox_scheduler.segments.segment.Segment])

Bases: qblox_scheduler.segments.segment.Segment

Experiment segment that loops other segments over some values.

property domains: dict[qblox_scheduler.operations.variables.Variable, qblox_scheduler.operations.loop_domains.LinearDomain]

Domains to loop over.

property segments: list[qblox_scheduler.segments.segment.Segment]

Segments to execute.

run(device: qblox_scheduler.device_under_test.QuantumDevice, timeout: int = 10) → xarray.Dataset | None

Execute segment on quantum device.

_unrolled_segments() → list[qblox_scheduler.schedules.schedule.TimeableScheduleBase | qblox_scheduler.segments.segment.Segment]

class Callback(callback: collections.abc.Callable[..., None], *args, **kwargs)

Bases: qblox_scheduler.segments.segment.Segment

Experiment segment that calls a generic, user-defined callback.

Example

def foo(bar, baz, qux):
    print(bar, baz, qux)

schedule = Schedule("test")
schedule.add(
    Callback(foo, 1, "a", qux=True)
)
schedule.add(Measure("q0"))

Parameters:

• callback – The callback function to be called in the run method.

• args – The arguments for the callback function.

• kwargs – The keyword arguments for the callback function.

property callback: collections.abc.Callable[..., None]

The callback function to be called in the run method.

property arguments: list

The arguments for the callback function.

property keyword_arguments: dict

The keyword arguments for the callback function.

run(device: qblox_scheduler.device_under_test.QuantumDevice, timeout: int) → None

Execute segment on quantum device.

class SetHardwareDescriptionField(name: str | int | tuple[str | int, ...], value: Any, instrument: str, create_new: bool = False)

Bases: qblox_scheduler.segments.segment.Segment

Experiment segment that sets a hardware description parameter for a given instrument.

Example

schedule = Schedule("test")
# corresponds to:
#   hardware_config = device.generate_hardware_compilation_config()
#   cluster0_description = hardware_config.hardware_description["cluster0"]
#   cluster0_description.modules[2].rf_output_on = False
schedule.add(
    SetHardwareDescriptionField(("modules", 2, "rf_output_on"), False, instrument="cluster0")
)
schedule.add(Measure("q0"))

Parameters:

• name –

  one of:

  • a str, corresponding to a hardware option on the port/clock.

  • a tuple of str, corresponding to a nested hardware option on the port/clock

• value – Value to set the parameter to.

• instrument – Instrument to set the parameter for.

• create_new – If True, create a new entry in the hardware configuration if no entry exists for this port-clock and hardware option. Otherwise, raise an error if the entry does not exist. Optional, by default False.

property instrument: str

Instrument to set field for.

property field: list[str | int]

Field path to set.

property value: Any

Field value to set.

property create_new: bool

Whether to create a new configuration field if it did not previously exist.

run(device: qblox_scheduler.device_under_test.QuantumDevice, timeout: int = 10) → None

Execute segment on quantum device.

class SetHardwareOption(name: str | int | tuple[str | int, ...], value: Any, port: str, create_new: bool = False)

Bases: qblox_scheduler.segments.segment.Segment

Experiment segment that sets a hardware option for a given port/clock.

Example

schedule = Schedule("resonator flux spectroscopy")
with schedule.loop(linspace(36e6, 38e6, 300, DType.FREQUENCY)) as lo_freq:
    # corresponds to:
    #   hardware_config = device.generate_hardware_compilation_config()
    #   hardware_options = hardware_config.hardware_options
    #   hardware_options.modulation_frequencies["q0:mw-q0.f_larmor"].lo_freq = lo_freq
    schedule.add(
        SetHardwareOption(("modulation_frequencies", "lo_freq"), lo_freq, port="q0:mw-q0.f_larmor")
    )
    schedule.add(Measure("q0"))

Parameters:

• name –

  One of:

  • a str, corresponding to a hardware option on the port/clock.

  • a tuple of str, corresponding to a nested hardware option on the port/clock

• value – Value to set the option to.

• port – Port/clock combination to set the option for.

• create_new – If True, create a new entry in the hardware configuration if no entry exists for this port-clock and hardware option. Otherwise, raise an error if the entry does not exist. Optional, by default False.

property port: str

Port/clock combination to set option for.

property option: list[str | int]

Option name to set.

property value: Any

Option value to set.

property create_new: bool

Whether to create a new configuration field if it did not previously exist.

run(device: qblox_scheduler.device_under_test.QuantumDevice, timeout: int = 10) → None

Execute segment on quantum device.

class SetParameter(name: qcodes.parameters.Parameter | str | int | tuple[str | int, ...], value: Any, element: str | None = None, create_new: bool = False)

Bases: qblox_scheduler.segments.segment.Segment

Experiment segment that sets a QCoDeS parameter, or device element parameter.

Examples

Set a QCoDeS parameter:

dc_offset = agent.get_clusters()["cluster0"].module4.out0_offset

schedule = Schedule("resonator flux spectroscopy")
with schedule.loop(linspace(0, 0.5, 30, DType.NUMBER)) as offset:
    schedule.add(SetParameter(dc_offset, offset))
    with schedule.loop(linspace(360e6, 380e6, 300, DType.FREQUENCY)) as freq:
        schedule.add(Reset("q0"))
        schedule.add(
            Measure("q0", freq=freq, coords={"frequency": freq, "dc_offset": offset})
        )
        schedule.add(IdlePulse(4e-9))

Set a device element parameter:

schedule = Schedule("hello")
with schedule.loop(linspace(0, 0.5, 3, DType.AMPLITUDE)) as amp:
    # corresponds to q0.measure.pulse_amp = amp
    schedule.add(SetParameter(("measure", "pulse_amp"), amp, element="q0"))
    schedule.add(Reset("q0"))
    schedule.add(
        Measure("q0", coords={"frequency": freq, "pulse_amp": amp})
    )

Parameters:

• name –

  One of:

  • QCoDeS parameter

  • a str, corresponding to a parameter on the quantum device.

  • a tuple of str, corresponding to a nested parameter on the quantum device or device element or edge.

• value – Value to set the parameter to.

• element – Optional. If provided, the parameter is set on the device element with the given name.

• create_new – If True, create a new entry in the device configuration if no entry exists for this port-clock and hardware option. Otherwise, raise an error if the entry does not exist. Optional, by default False.

property element: str | None

Element to set QCoDeS parameter on.

property parameter: list[str | int] | qcodes.parameters.Parameter

QCoDeS parameter name to set.

property value: Any

QCoDeS parameter value to set.

property create_new: bool

Whether to create a new parameter if it did not previously exist.

run(device: qblox_scheduler.device_under_test.QuantumDevice, timeout: int = 10) → None

Execute segment on quantum device.

class Numbers

Bases: TypedDict

Dict used to emulate the behaviour of the Numbers qcodes validator.

min_value: float

max_value: float

allow_nan: bool

Parameter(*, initial_value: Any = _Unset, label: str | None = _Unset, docstring: str | None = _Unset, unit: str | None = _Unset, vals: Numbers | None = _Unset, **kwargs: Any) → Any

Wrapper function around :func:~pydantic.Field that tries to emulate qcodes parameters as closely as possible, to facilitate migration and reduce diff lines.

Parameters:

• initial_value – Maps to default or default_factory (if callable).

• label – Maps to title, displayed on the JSON schema.

• docstring – Maps to description, displayed on the JSON schema.

• unit – Stored internally, retrievable using get_unit().

• vals – Maps to allow_inf_nan, ge and le.

• kwargs – Other arguments passed on to the original Field function.

Returns:

Any: To appease the linters; actually a :class:~pydantic.fields.FieldInfo instance.

class SchedulerSubmodule(/, name, *, parent: SchedulerBaseModel | None = None, **data: Any)

Bases: SchedulerBaseModel

Compatibility class emulating the behaviour of InstrumentModule/InstrumentChannel.

_parent: SchedulerBaseModel | SchedulerSubmodule | None

property parent: SchedulerBaseModel | None

classmethod _drop_reference_magnitude(data: Any) → Any