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https://hdl.handle.net/11499/56995
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DC Field | Value | Language |
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dc.contributor.author | Alcazar, Javier | - |
dc.contributor.author | Vakili, Mohammad Ghazi | - |
dc.contributor.author | Kalayci, Can B. | - |
dc.contributor.author | Perdomo-Ortiz, Alejandro | - |
dc.date.accessioned | 2024-05-06T16:24:36Z | - |
dc.date.available | 2024-05-06T16:24:36Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://doi.org/10.1038/s41467-024-46959-5 | - |
dc.identifier.uri | https://hdl.handle.net/11499/56995 | - |
dc.description.abstract | Devising an efficient exploration of the search space is one of the key challenges in the design of combinatorial optimization algorithms. Here, we introduce the Generator-Enhanced Optimization (GEO) strategy: a framework that leverages any generative model (classical, quantum, or quantum-inspired) to solve optimization problems. We focus on a quantum-inspired version of GEO relying on tensor-network Born machines, and referred to hereafter as TN-GEO. To illustrate our results, we run these benchmarks in the context of the canonical cardinality-constrained portfolio optimization problem by constructing instances from the S&P 500 and several other financial stock indexes, and demonstrate how the generalization capabilities of these quantum-inspired generative models can provide real value in the context of an industrial application. We also comprehensively compare state-of-the-art algorithms and show that TN-GEO is among the best; a remarkable outcome given the solvers used in the comparison have been fine-tuned for decades in this real-world industrial application. Also, a promising step toward a practical advantage with quantum-inspired models and, subsequently, with quantum generative models Solving combinatorial optimization problems using quantum or quantum-inspired machine learning models would benefit from strategies able to work with arbitrary objective functions. Here, the authors use the power of generative models to realise such a black-box solver, and show promising performances on some portfolio optimization examples. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Nature Portfolio | en_US |
dc.relation.ispartof | Nature Communications | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Portfolio | en_US |
dc.subject | Algorithm | en_US |
dc.title | Enhancing combinatorial optimization with classical and quantum generative models | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 1 | en_US |
dc.department | Pamukkale University | en_US |
dc.authorid | Kalayci, Can/0000-0003-2355-7015 | - |
dc.authorid | Ghazi Vakili, mohammad/0000-0002-2927-4975 | - |
dc.authorid | Perdomo-Ortiz, Alejandro/0000-0001-7176-4719 | - |
dc.identifier.doi | 10.1038/s41467-024-46959-5 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57219622144 | - |
dc.authorscopusid | 57211116206 | - |
dc.authorscopusid | 54951330900 | - |
dc.authorscopusid | 35747037700 | - |
dc.authorwosid | Kalayci, Can/K-5884-2013 | - |
dc.authorwosid | Perdomo-Ortiz, Alejandro/B-4753-2009 | - |
dc.identifier.pmid | 38553469 | en_US |
dc.identifier.scopus | 2-s2.0-85189016688 | en_US |
dc.identifier.wos | WOS:001195597700025 | en_US |
dc.institutionauthor | … | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 10.09. Industrial Engineering | - |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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s41467-024-46959-5.pdf | 1.22 MB | Adobe PDF | View/Open |
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