MQT Bench: Benchmarking Software And Design Automation Tools For Quantum Computing

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Nils Quetschlich¹, Lukas Burgholzer¹, and Robert Wille^1,2

¹Chair for Design Automation, Technical University of Munich, Germany
²Software Competence Center Hagenberg GmbH (SCCH), Austria

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Abstract

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 $textit{MQT Bench}$ benchmark suite (as part of the $textit{Munich Quantum Toolkit}$, 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.

MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing PlatoBlockchain Data Intelligence. Vertical Search. Ai.

Featured image: User interface of the MQT Bench website (https://www.cda.cit.tum.de/mqtbench/).

MQT Bench comes as an easy-to-use website and a Python Package while its implementation is open-source available on GitHub.

Popular summary

To utilize quantum computers for the different application domains, the respective to-be-solved problem must be encoded into a quantum circuit. Afterwards, that circuit needs to be executed to determine the desired solution. For that, quantum software tools are essential, e.g., to classically simulate the considered quantum circuit or to compile it before it can be executed on a quantum computer. Whenever such a quantum software tool is proposed, it is important to empirically evaluate its performance and to compare it to the state of the art. For that purpose, MQT Bench (as part of the Munich Quantum Toolkit, MQT) is proposed. MQT Bench provides over 70,000 benchmarks on various abstraction levels (depending on what level the to-be-evaluated software tool operates on) ranging from 2 to 130 qubits aiming for increased comparability, reproducibility, and transparency.

► BibTeX data

@article{Quetschlich2023mqtbench, doi = {10.22331/q-2023-07-20-1062}, url = {https://doi.org/10.22331/q-2023-07-20-1062}, title = {{MQT} {B}ench: {B}enchmarking {S}oftware and {D}esign {A}utomation {T}ools for {Q}uantum {C}omputing}, author = {Quetschlich, Nils and Burgholzer, Lukas and Wille, Robert}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {7}, pages = {1062}, month = jul, year = {2023}<br /> }

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Cited by

[1] Mirko Amico, Helena Zhang, Petar Jurcevic, Lev S. Bishop, Paul Nation, Andrew Wack, and David C. McKay, “Defining Standard Strategies for Quantum Benchmarks”, arXiv:2303.02108, (2023).

[2] Robert Wille and Lukas Burgholzer, “MQT QMAP: Efficient Quantum Circuit Mapping”, arXiv:2301.11935, (2023).

[3] Nils Quetschlich, Lukas Burgholzer, and Robert Wille, “Predicting Good Quantum Circuit Compilation Options”, arXiv:2210.08027, (2022).

[4] Tom Peham, Lukas Burgholzer, and Robert Wille, “Equivalence Checking of Quantum Circuits With the ZX-Calculus”, IEEE Journal on Emerging and Selected Topics in Circuits and Systems 12 3, 662 (2022).

[5] Nils Quetschlich, Lukas Burgholzer, and Robert Wille, “Compiler Optimization for Quantum Computing Using Reinforcement Learning”, arXiv:2212.04508, (2022).

[6] Tom Peham, Lukas Burgholzer, and Robert Wille, “On Optimal Subarchitectures for Quantum Circuit Mapping”, arXiv:2210.09321, (2022).

[7] Lukas Burgholzer, Alexander Ploier, and Robert Wille, “Simulation Paths for Quantum Circuit Simulation with Decision Diagrams”, arXiv:2203.00703, (2022).

[8] Konrad Jałowiecki, Paulina Lewandowska, and Łukasz Pawela, “PyQBench: a Python library for benchmarking gate-based quantum computers”, arXiv:2304.00045, (2023).

[9] Jingcheng Shen, Linbo Long, Masao Okita, and Fumihiko Ino, “A Reorder Trick for Decision Diagram Based Quantum Circuit Simulation”, arXiv:2211.07110, (2022).

The above citations are from SAO/NASA ADS (last updated successfully 2023-07-21 02:27:09). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2023-07-21 02:27:08).

This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.

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Source: https://quantum-journal.org/papers/q-2023-07-20-1062/

Time Stamp: July 20, 2023

Time Stamp: Sep 8, 2022

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

Republished By Plato

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