Software for Creating Scalable Benchmarks from Quantum Algorithms

Quantum Physics arXiv:2511.02134 (quant-ph) [Submitted on 3 Nov 2025] Title:Software for Creating Scalable Benchmarks from Quantum Algorithms Authors:Noah Siekierski, Stefan Seritan, Neer Patel, Siyuan Niu, Thomas Lubinski, Timothy Proctor View a PDF of the paper titled Software for Creating Scalable Benchmarks from Quantum Algorithms, by Noah Siekierski and 5 other authors View PDF HTML (experimental) Abstract:Creating scalable, reliable, and well-motivated benchmarks for quantum computers is challenging: straightforward approaches to benchmarking suffer from exponential scaling, are insensitive to important errors, or use poorly-motivated performance metrics. Furthermore, curated benchmarking suites cannot include every interesting quantum circuit or algorithm, which necessitates a tool that enables the easy creation of new benchmarks. In this work, we introduce a software tool for creating scalable and reliable benchmarks that measure a well-motivated performance metric (process fidelity) from user-chosen quantum circuits and algorithms. Our software, called $\texttt{scarab}$, enables the creation of efficient and robust benchmarks even from circuits containing thousands or millions of qubits, by employing efficient fidelity estimation techniques, including mirror circuit fidelity estimation and subcircuit volumetric benchmarking. $\texttt{scarab}$ provides a simple interface that enables the creation of reliable benchmarks by users who are not experts in the theory of quantum computer benchmarking or noise. We demonstrate the flexibility and power of $\texttt{scarab}$ by using it to turn existing inefficient benchmarks into efficient benchmarks, to create benchmarks that interrogate hardware and algorithmic trade-offs in Hamiltonian simulation, to quantify the in-situ efficacy of approximate circuit compilation, and to create benchmarks that use subcircuits to measure progress towards executing a circuit of interest. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2511.02134 [quant-ph] (or arXiv:2511.02134v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2511.02134 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Noah Siekierski [view email] [v1] Mon, 3 Nov 2025 23:53:42 UTC (3,613 KB) Full-text links: Access Paper: View a PDF of the paper titled Software for Creating Scalable Benchmarks from Quantum Algorithms, by Noah Siekierski and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-11 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)