qibocal.protocols.drag package#

Submodules#

qibocal.protocols.drag.drag module#

qibocal.protocols.drag.drag.drag_tuning = Routine(acquisition=<function _acquisition>, fit=<function _fit>, report=<function _plot>, update=<function _update>, two_qubit_gates=False)#: DragTuning Routine object.

class qibocal.protocols.drag.drag.DragTuningParameters(beta_start: float, beta_end: float, beta_step: float, unrolling: bool = False, nflips: int = 1)[source]#

Bases: Parameters

DragTuning runcard inputs.

beta_start: float#: DRAG pulse beta start sweep parameter.

beta_end: float#: DRAG pulse beta end sweep parameter.

beta_step: float#: DRAG pulse beta sweep step parameter.

unrolling: bool = False#: If True it uses sequence unrolling to deploy multiple sequences in a single instrument call. Defaults to False.

nflips: int = 1#: Repetitions of (Xpi - Xmpi).

hardware_average: bool = False#: By default hardware average will be performed.

nshots: int#: Number of executions on hardware.

relaxation_time: float#: Wait time for the qubit to decohere back to the gnd state.

class qibocal.protocols.drag.drag.DragTuningResults(betas: dict[typing.Union[int, str], float], fitted_parameters: dict[typing.Union[int, str], dict[str, float]], chi2: dict[typing.Union[int, str], tuple[float, typing.Optional[float]]] = <factory>)[source]#

Bases: Results

DragTuning outputs.

betas: dict[Union[int, str], float]#: Optimal beta paramter for each qubit.

fitted_parameters: dict[Union[int, str], dict[str, float]]#: Raw fitting output.

chi2: dict[Union[int, str], tuple[float, Optional[float]]]#: Chi2 calculation.

_to_json(path: Path, filename: str)#: Helper function to dump to json.

_to_npz(path: Path, filename: str)#: Helper function to use np.savez while converting keys into strings.

static load_data(path: Path, filename: str)#: Load data stored in a npz file.

static load_params(path: Path, filename: str)#: Load parameters stored in a json file.

property params: dict#: Convert non-arrays attributes into dict.

save(path: Path)#: Store results to file.

class qibocal.protocols.drag.drag.DragTuningData(data: dict[typing.Union[int, str], numpy.ndarray[typing.Any, numpy.dtype[dtype([('prob', '<f8'), ('error', '<f8'), ('beta', '<f8')])]]] = <factory>)[source]#

Bases: Data

DragTuning acquisition outputs.

_to_json(path: Path, filename: str)#: Helper function to dump to json.

_to_npz(path: Path, filename: str)#: Helper function to use np.savez while converting keys into strings.

static load_data(path: Path, filename: str)#: Load data stored in a npz file.

static load_params(path: Path, filename: str)#: Load parameters stored in a json file.

property pairs#: Access qubit pairs ordered alphanumerically from data structure.

property params: dict#: Convert non-arrays attributes into dict.

property qubits#: Access qubits from data structure.

register_qubit(dtype, data_keys, data_dict)#

Store output for single qubit.

Parameters:

data_keys (tuple) – Keys of Data.data.
data_dict (dict) – The keys are the fields of dtype and
arrays. (the values are the related) –

save(path: Path)#: Store data to file.

data: dict[typing.Union[int, str], numpy.ndarray[typing.Any, numpy.dtype[dtype([('prob', '<f8'), ('error', '<f8'), ('beta', '<f8')])]]]#: Raw data acquired.

qibocal.protocols.drag.drag_simple module#

qibocal.protocols.drag.drag_simple.drag_simple = Routine(acquisition=<function _acquisition>, fit=<function _fit>, report=<function _plot>, update=<function _update>, two_qubit_gates=False)#: DragTuning Routine object.