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MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing

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arxiv 2204.13719 v3 pith:LHBUS37G submitted 2022-04-28 quant-ph cs.ET

classification quant-phcs.ET
keywords quantumabstractionbenchmarklevelssoftwaretoolsbenchbenchmarks
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantum software tools for a wide variety of design tasks on and across different levels of abstraction are crucial in order to eventually realize useful quantum applications. This requires practical and relevant benchmarks for new software tools to be empirically evaluated and compared to the current state of the art. Although benchmarks for specific design tasks are commonly available, the demand for an overarching cross-level benchmark suite has not yet been fully met and there is no mutual consolidation in how quantum software tools are evaluated thus far. In this work, we propose the MQT Bench benchmark suite (as part of the Munich Quantum Toolkit, MQT) based on four core traits: (1) cross-level support for different abstraction levels, (2) accessibility via an easy-to-use web interface (https://www.cda.cit.tum.de/mqtbench) and a Python package, (3) provision of a broad selection of benchmarks to facilitate generalizability, as well as (4) extendability to future algorithms, gate-sets, and hardware architectures. By comprising more than 70,000 benchmark circuits ranging from 2 to 130 qubits on four abstraction levels, MQT Bench presents a first step towards benchmarking different abstraction levels with a single benchmark suite to increase comparability, reproducibility, and transparency.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Comparing and learning figures of merit for quantum circuit compilation

    quant-ph 2026-07 conditional novelty 7.0 of 10

    ML models that fuse circuit structure with device coherence data predict weighted PST far more accurately than classical gate-count FoMs, enabling better circuit selection inside compilers.

  2. Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization

    quant-ph 2026-07 conditional novelty 6.0 of 10

    A learning-to-rank model over feature-model-sampled Qiskit transpiler pass configurations reliably outperforms Qiskit's fixed optimization levels on two-qubit gate reduction.

  3. Automatic Qiskit Code Refactoring Using Large Language Models

    cs.SE 2025-06 conditional novelty 5.0 of 10

    A structured taxonomy of Qiskit migration scenarios improves GPT-4's line-level refactoring precision from 0.32 to 0.55 and recall from 0.35 to 0.62 on 25 synthetic snippets.

  4. Localized Kernel Methods for Signal Processing

    eess.SP 2025-08 reject novelty 2.0 of 10

    The manuscript is internally inconsistent: the abstract describes localized kernel signal processing, while the body is a different paper on quantum task scheduling, leaving the abstract's claims entirely unsupported.

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