iqm.benchmarks.optimization.qscore.QScoreBenchmark

class iqm.benchmarks.optimization.qscore.QScoreBenchmark(backend_arg: IQMBackendBase, configuration: QScoreConfiguration)

Bases: Benchmark

Q-score estimates the size of combinatorial optimization problems a given number of qubits can execute with meaningful results.

Attributes

name

Methods

add_all_meta_to_dataset(dataset)	Adds all configuration metadata and circuits to the dataset variable
analysis_function(run)	Analysis function for a QScore experiment
choose_qubits_custom(num_qubits)	Choose the qubits to execute the circuits on, according to elements in custom_qubits_array matching num_qubits number of qubits
choose_qubits_naive(num_qubits)	Choose the qubits to execute the circuits on, sequentially starting at qubit 0.
execute(backend)	Executes the benchmark.
generate_maxcut_ansatz(graph, theta[, rzz_list])	Generate an ansatz circuit for QAOA MaxCut, with measurements at the end.
generate_maxcut_ansatz_star(graph, theta)	Generate an ansatz circuit for QAOA MaxCut, with measurements at the end.
greedy_vertex_cover_with_mapping(G)	Approximate a minimum vertex cover for a given graph, providing a mapping of nodes to the edges they cover.

Parameters:

• backend_arg (IQMBackendBase) –

• configuration (QScoreConfiguration) –

static analysis_function(run: BenchmarkRunResult) → BenchmarkAnalysisResult

Analysis function for a QScore experiment

Parameters:

run (RunResult) – A QScore experiment run for which analysis result is created

Returns:

AnalysisResult corresponding to QScore

Return type:

BenchmarkAnalysisResult

greedy_vertex_cover_with_mapping(G: Graph)

Approximate a minimum vertex cover for a given graph, providing a mapping of nodes to the edges they cover.

Parameters:

G (nx.Graph) – The input graph for which the vertex cover is to be computed.

Returns:

A dictionary where keys are nodes and values are lists of edges that each node covers.

Return type:

dict

generate_maxcut_ansatz_star(graph, theta)

Generate an ansatz circuit for QAOA MaxCut, with measurements at the end.

Parameters:

• graph (networkx graph) – the MaxCut problem graph

• theta (list[float]) – the variational parameters for QAOA, first gammas then betas

Returns:

the QAOA ansatz quantum circuit.

Return type:

QuantumCircuit

generate_maxcut_ansatz(graph: Graph, theta: list[float], rzz_list=None) → QuantumCircuit

Generate an ansatz circuit for QAOA MaxCut, with measurements at the end.

Parameters:

• graph (networkx graph) – the MaxCut problem graph

• theta (list[float]) – the variational parameters for QAOA, first gammas then betas

Returns:

the QAOA ansatz quantum circuit.

Return type:

QuantumCircuit

add_all_meta_to_dataset(dataset: Dataset)

Adds all configuration metadata and circuits to the dataset variable

Parameters:

dataset (xr.Dataset) – The xarray dataset

static choose_qubits_naive(num_qubits: int) → list[int]

Choose the qubits to execute the circuits on, sequentially starting at qubit 0.

Parameters:

num_qubits (int) – the number of qubits to choose.

Returns:

the list of qubits to execute the circuits on.

Return type:

list[int]

choose_qubits_custom(num_qubits: int) → list[int]

Choose the qubits to execute the circuits on, according to elements in custom_qubits_array matching num_qubits number of qubits

Parameters:

num_qubits (int) – the number of qubits to choose

Returns:

the list of qubits to execute the circuits on

Return type:

list[int]

execute(backend: IQMBackendBase) → Dataset

Executes the benchmark.

Parameters:

backend (IQMBackendBase) –

Return type:

Dataset