qcmet.benchmarks.BenchmarkCollection

class qcmet.benchmarks.BenchmarkCollection(benchmarks, save_path=None)

A collection of BaseBenchmark instances.

This class defines a wrapper for collecting different benchmarks into one. This class inherits from BaseBenchmark, providing the same API as other benchmarks.

__init__(benchmarks, save_path=None)

Initialize a BenchmarkCollection instance.

Parameters:

• benchmarks (List[BaseBenchmark] | Dict[str, BaseBenchmark]) – Either: - List[BaseBenchmark]: List of BaseBenchmark instances. - Dict[str, BaseBenchmark]: Dictionary of {identifier: BaseBenchmark}. The label of each benchmark will be created in the format of “Benchmark{index}_{benchmark.name}” if no identifiers provided or as “identifier” if the benchmarks are passed in as a dictionary.

• save_path (str | Path | FileManager, optional) – Path to save benchmark outputs. Defaults to None.

Methods

__init__(benchmarks[, save_path])	Initialize a BenchmarkCollection instance.
analyze()	Analyze measurements to return benchmark results.
generate_circuits()	Generate benchmark circuits, user facing.
has_plotting()	Check if _plot function is implemented for at least one of the benchmarks in the collection.
load_circuit_measurements(circuit_measurements)	Load measurement counts into the experiment_data DataFrame.
measurements_to_probabilities()	Convert raw measurement counts to normalized probabilities.
plot([axes])	Plot benchmark results for all benchmarks in the collection that has a plot function.
run([device, num_shots])	Run all benchmarks in the collection.
save()	Call each benchmark in the collection to save the results.
set_save_path(save_path)	Set benchmark save path if not set in class constructor.

Attributes

circuits	Gets all benchmark circuits.
experiment_data	Getter for experiment_data dataframe.
num_qubits	Return the number of qubits for each benchmark in the collection.

property num_qubits: Dict

Return the number of qubits for each benchmark in the collection. Overrides BaseBenchmark.num_qubits.

run(device=None, num_shots=1024, **kwargs)

Run all benchmarks in the collection. Overrides BaseBenchmark.run.

If num_shots is int, super().run() is called to run all circuits from all benchmarks in one go. Otherwise, each benchmark is called to run in sequence with a different specified number of shots, and self._experiment_data and self._runtime_params will be left as None, since each benchmark will have their own _experiment_data and _runtime_params generated by their respective run.

Parameters:

• device (BaseDevice, optional) – Device to run benchmark on. Defaults to None.

• num_shots (int | List[int], optional) – Either - int: Number of shots, applied to all benchmarks in the collection. - List[int]: A list of number of shots for each benchmark in the collection. Defaults to 1024.

• **kwargs (Dict[str, any]) – Optional keyword arguments passed to device in _runtime_params.

save()

Call each benchmark in the collection to save the results.

has_plotting()

Check if _plot function is implemented for at least one of the benchmarks in the collection.

Overrides BaseBenchmark.has_plotting.

Returns:

True if at least one of the benchmarks in the collection have a plot function.

Return type:

bool

plot(axes=None)

Plot benchmark results for all benchmarks in the collection that has a plot function.

Parameters:

axes (List[matplotlib.axes._axes.Axes], optional) – Plot will use axes if provided. The length of this list must be equal to the number of benchmarks in the collection with plotting implemented. Defaults to None.