iqm.benchmarks.compressive_gst.compressive_gst.CompressiveGST

class iqm.benchmarks.compressive_gst.compressive_gst.CompressiveGST(backend: IQMBackendBase, configuration: GSTConfiguration)

Bases: Benchmark

SPAM-robust characterization of a set of quantum gates

Attributes

name

Methods

add_configuration_to_dataset(dataset)	Creates an xarray.Dataset and adds the circuits and configuration metadata to it
analysis_function(run)	Analysis function for compressive GST
execute(backend)	The main GST execution routine
generate_meas_circuits()	Generate random circuits from the gate set

Parameters:

• backend (IQMBackendBase) –

• configuration (GSTConfiguration) –

static analysis_function(run: BenchmarkRunResult) → BenchmarkAnalysisResult

Analysis function for compressive GST

Parameters:

run (BenchmarkRunResult) – BenchmarkRunResult A BenchmarkRunResult instance storing the dataset

Returns:

BenchmarkAnalysisResult

An BenchmarkAnalysisResult instance with the updated dataset, as well as plots and observations

Return type:

result

generate_meas_circuits() → tuple[BenchmarkCircuit, BenchmarkCircuit]

Generate random circuits from the gate set

The random circuits are distributed among different depths ranging from L_MIN to L_MAX, both are configurable and stored in self.configuration.seq_len_list. No transpilation other than mapping to the desired qubits is performed, as the gates need to be executed axactly as described for GST to give meaningful results

Returns:

BenchmarkCircuit

The transpiled circuits

untranspiled_circuits: BenchmarkCircuit

The untranspiled circuits

circuit_gen_transp_time: float

The time it took to generate and transpile the circuits

Return type:

transpiled_circuits

add_configuration_to_dataset(dataset)

Creates an xarray.Dataset and adds the circuits and configuration metadata to it

Parameters:

self – Source class

Returns:

xarray.Dataset to be used for further data storage

Return type:

dataset

execute(backend) → Dataset

The main GST execution routine

Return type:

Dataset