PyQBench: Open-Source Python Tool Certifies Qubit Measurements on Noisy Devices.

PyQBench, an open-source Python library, now certifies qubit von Neumann measurements on Noisy Intermediate-Scale Quantum (NISQ) devices. The updated version integrates with Qiskit, facilitating benchmarking on real quantum hardware and supporting custom measurement schemes to assess and improve measurement fidelity and error mitigation.

Assessing the reliability of quantum computations necessitates rigorous evaluation of individual quantum components, particularly the accuracy of qubit measurements. Imperfect measurements introduce errors that rapidly accumulate, hindering the potential of near-term quantum devices. To address this, researchers are developing tools to characterise measurement fidelity on noisy intermediate-scale quantum (NISQ) computers. A new iteration of the open-source Python library, PyQBench, facilitates this process. Martin Beseda (Università dell’Aquila) and Paulina Lewandowska (IT4Innovations, VSB – Technical University of Ostrava) detail these advancements in their article, “Benchmarking gate-based quantum devices via certification of qubit von Neumann measurements”, presenting a flexible and extensible platform for benchmarking quantum hardware and validating measurement processes.

Assessing Quantum Measurement Accuracy with PyQBench

PyQBench, an open-source Python library, offers a comprehensive toolkit for benchmarking gate-based quantum computers, with a specific focus on verifying the accuracy of qubit measurements. The latest version introduces a certification scheme to evaluate measurement fidelity on Noisy Intermediate-Scale Quantum (NISQ) devices, extending its original capability to discriminate between von Neumann measurements – a fundamental process in quantum information processing where a quantum state collapses upon measurement. The library functions via both a command-line interface and a Python API, allowing users to implement bespoke measurement schemes and incorporate diverse error models for advanced benchmarking tailored to specific hardware and experimental conditions.

Benchmarking experiments utilising PyQBench demonstrate reproducible results. Raw measurement histograms, representing the frequency of each outcome, remain consistent across multiple repetitions, as do their error-mitigated counterparts. Error mitigation transforms these raw counts into probabilities, revealing a discernible bias towards the ‘00’ and ‘01’ outcomes. The parameter ‘Φ’ (phi), representing a circuit parameter, varies between runs, indicating systematic exploration of the circuit’s behaviour under differing conditions.

The library’s open-source nature fosters community collaboration and accelerates advancements in hardware benchmarking, promoting a transparent and collaborative approach to quantum computing development. Reliable performance metrics are crucial for validating the efficacy of error mitigation techniques, which aim to reduce the impact of noise on quantum computations. PyQBench contributes to the ongoing effort to build more reliable and scalable quantum computers by providing tools to confidently assess and improve hardware performance, thereby accelerating progress towards practical quantum computation.

Data analysis demonstrates consistent application of error mitigation to a quantum circuit benchmarking experiment. Across multiple runs, the mitigation process successfully reshapes raw measurement counts into a stable probability distribution. The probabilities for outcomes ‘00’ and ‘01’ consistently dominate, suggesting the circuit effectively prepares or measures these states, despite initial variations in raw counts. The consistency of mitigated probabilities across runs indicates experimental stability and reliability in the applied error correction.

Further interpretation requires detailed knowledge of the specific quantum circuit, the error mitigation technique employed, and the experimental setup. While initial raw counts vary significantly between runs – notably in Run 3 – the mitigation process converges the results towards a consistent probability distribution. This suggests the technique successfully addresses systematic errors or biases present in the measurement process. Future work should quantify the precise impact of the error mitigation, determining the reduction in error rates achieved. This will provide a valuable metric for evaluating both the quantum hardware and the mitigation technique’s effectiveness.

Further investigation could explore the scalability of this mitigation strategy to more complex circuits and larger qubit systems. Detailed analysis of the parameters (Φ and Δ – delta) and their influence on the mitigation process would provide a deeper understanding of the underlying mechanisms at play.