{"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/leveraging-quantum-machine-learning","title":"Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation","arxiv_id":"2405.12866","date":"2024-05-21","proceeding":null,"authors":["Alon Kukliansky","Lukasz Cincio","Ed Younis","Costin Iancu"],"abstract":"Existing numerical optimizers deployed in quantum compilers use expensive $\\mathcal{O}(4^n)$ matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler $\\mathcal{O}(2^n)$ circuit simulations on a set of sample inputs. The simpler the circuit, the lower the number of required input samples. We validate QFactor-Sample on a large set of circuits and discuss its hyperparameter tuning. When incorporated in the BQSKit quantum compiler and compared against a state-of-the-art domain-specific optimizer, We demonstrate improved scalability and a reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits. We also discuss how improved numerical optimization affects the dynamics of partitioning-based compilation schemes, which allow a trade-off between compilation speed and solution quality.","url_abs":"https://arxiv.org/abs/2405.12866v2","url_pdf":"https://arxiv.org/pdf/2405.12866v2.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":"leveraging-quantum-machine-learning","repo_url":"https://github.com/BQSKit/bqskit-qfactor-jax","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","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}