iqm.benchmarks.compressive_gst.compressive_gst.CompressiveGST

iqm.benchmarks.compressive_gst.compressive_gst.CompressiveGST#

class iqm.benchmarks.compressive_gst.compressive_gst.CompressiveGST(backend: IQMBackend | IQMFacadeBackend | str, configuration: GSTConfiguration)#

Bases: Benchmark

SPAM-robust characterization of a set of quantum gates.

Attributes

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.

name()

Returns the name of the benchmark.

validate_gate_context()

Validate that the gate context is properly configured.
Parameters:
static analysis_function(run: BenchmarkRunResult) → BenchmarkAnalysisResult#

Analysis function for compressive GST.

Parameters:

run (BenchmarkRunResult) – A BenchmarkRunResult instance storing the dataset

Returns:

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

Return type:

result

classmethod name() → str#

Returns the name of the benchmark.

Return type:

str

validate_gate_context() → None#

Validate that the gate context is properly configured.

Checks that: 1. If gate_context is a list, it has the same length as gate_set 2. The qubits used in gate_context don’t overlap with qubits in the layouts

Raises:

ValueError – If gate_context is invalid or if qubits in gate_context overlap with qubits in layouts

Return type:

None

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:

The transpiled circuits untranspiled_circuits: The untranspiled circuits circuit_gen_transp_time: The time it took to generate and transpile the circuits

Return type:

transpiled_circuits

add_configuration_to_dataset(dataset: Dataset) → None#

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

Parameters:

  • self – Source class

  • dataset (Dataset) – Dataset to which the configuration is added

Returns:

Dataset to be used for further data storage

Return type:

dataset

execute(backend: IQMBackend | IQMFacadeBackend) → Dataset#

The main GST execution routine.

Parameters:

backend (IQMBackend | IQMFacadeBackend)

Return type:

Dataset

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