open-qbench 0.2.0

Package for developing and executing quantum computing benchmarks

Open QBench

Open QBench is an open-source software framework for defining and executing benchmarks across the quantum-classical stack. It offers support for benchmarks on the:

• hybrid level (quantum-classical workflows)
• high level (quantum algorithms)
• low level (compiled quantum programs)

The framework is supports gate-based quantum computers, photonic systems (boson samplers), and quantum annealers.

Beyond the framework, this package also includes a suite of pre-implemented, high-level quantum application benchmarks. These are specifically crafted to evaluate the performance and fidelity of results on diverse physical quantum devices.

Installation

Using uv (Recommended)

First, create and activate a virtual environment:

uv venv

To install the core dependencies of the package, run:

uv sync

Optional dependencies (Extras)

We provide a number of optional dependencies (referred to as "extras") for executing specific benchmarks or for enabling support for various quantum hardware providers.

Available extras include:

• Benchmarks: VQE, QSVM
• Providers: IBM, AQT, ORCA

To install specific optional dependencies, use the --extra flag. You can specify multiple extras in a single command. For example, to run the VQE benchmark on an IBM Quantum machine, you would run:

uv sync --extra VQE --extra IBM

You can combine any of the available extras as needed.

[!NOTE] To install the ORCA extra, you'll first need to add your SSH key to the ORCA Computing SDK website at https://sdk.orcacomputing.com/.

Using pip

The package can also be installed with pip. To install the core dependencies run:

pip install .

To install with specific optional dependencies (e.g., VQE and IBM):

pip install ".[VQE,IBM]"

Usage

How to run a benchmark

This example shows how to execute a simple application benchmark using a Grover circuit on simulated IBM Quantum hardware (check Installation to see how to enable IBM support).

First define samplers used for collecting distributions.

from qiskit_aer.primitives import SamplerV2 as AerSampler
from qiskit_ibm_runtime import Sampler
from qiskit_ibm_runtime.fake_provider import FakeGeneva

ideal_sampler = AerSampler(default_shots=1000)
backend_sampler = Sampler(FakeGeneva())

Then use Open QBench to generate a quantum circuit and create your benchmark by defining input and a function used to calculate fidelity.

from open_qbench import ApplicationBenchmark
from open_qbench.apps import grover
from open_qbench.core import BenchmarkInput
from open_qbench.fidelities import normalized_fidelity

qc = grover.grover_nq(3, 6)
backend = backend_sampler.backend()
benchmark_input = BenchmarkInput(qc, backend)

ab = ApplicationBenchmark(
    backend_sampler,
    ideal_sampler,
    benchmark_input,
    name="Grover_benchmark",
    accuracy_measure=normalized_fidelity,
)

ab.run()
print(ab.result)

Contributing

We welcome contributions from the community! Please see CONTRIBUTING.md for a detailed guide.

License

Apache License 2.0