{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/qpack-scores-quantitative-performance-metrics","title":"QPack Scores: Quantitative performance metrics for application-oriented quantum computer benchmarking","arxiv_id":"2205.12142","date":"2022-05-24","proceeding":null,"authors":["Huub Donkers","Koen Mesman","Zaid Al-Ars","Matthias Möller"],"abstract":"This paper presents the benchmark score definitions of QPack, an application-oriented cross-platform benchmarking suite for quantum computers and simulators, which makes use of scalable Quantum Approximate Optimization Algorithm and Variational Quantum Eigensolver applications. Using a varied set of benchmark applications, an insight of how well a quantum computer or its simulator performs on a general NISQ-era application can be quantitatively made. This paper presents what quantum execution data can be collected and transformed into benchmark scores for application-oriented quantum benchmarking. Definitions are given for an overall benchmark score, as well as sub-scores based on runtime, accuracy, scalability and capacity performance. Using these scores, a comparison is made between various quantum computer simulators, running both locally and on vendors' remote cloud services. We also use the QPack benchmark to collect a small set of quantum execution data of the IBMQ Nairobi quantum processor. The goal of the QPack benchmark scores is to give a holistic insight into quantum performance and the ability to make easy and quick comparisons between different quantum computers","url_abs":"https://arxiv.org/abs/2205.12142v1","url_pdf":"https://arxiv.org/pdf/2205.12142v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"qpack-scores-quantitative-performance-metrics","repo_url":"https://gitlab.com/libket/qpack","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}