{"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/unsupervised-machine-learning-on-a-hybrid","title":"Unsupervised Machine Learning on a Hybrid Quantum Computer","arxiv_id":"1712.05771","date":"2017-12-15","proceeding":null,"authors":["J. S. Otterbach","R. Manenti","N. Alidoust","A. Bestwick","M. Block","B. Bloom","S. Caldwell","N. Didier","E. Schuyler Fried","S. Hong","P. Karalekas","C. B. Osborn","A. Papageorge","E. C. Peterson","G. Prawiroatmodjo","N. Rubin","Colm A. Ryan","D. Scarabelli","M. Scheer","E. A. Sete","P. Sivarajah","Robert S. Smith","A. Staley","N. Tezak","W. J. Zeng","A. Hudson","Blake R. Johnson","M. Reagor","M. P. da Silva","C. Rigetti"],"abstract":"Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.","url_abs":"http://arxiv.org/abs/1712.05771v1","url_pdf":"http://arxiv.org/pdf/1712.05771v1.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":"unsupervised-machine-learning-on-a-hybrid","repo_url":"https://github.com/BOHRTECHNOLOGY/quantum_tsp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.05771","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}