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dc.contributor.authorArrazola, Juan Miguel
dc.contributor.authorDelgado, Alain
dc.contributor.authorBardhan, Bhaskar Roy
dc.contributor.authorLloyd, Seth
dc.date.accessioned2022-01-11T15:08:54Z
dc.date.available2022-01-11T15:08:54Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/138871
dc.description.abstract© The Author(s), 2020. We study the practical performance of quantum-inspired algorithms for recommendation systems and linear systems of equations. These algorithms were shown to have an exponential asymptotic speedup compared to previously known classical methods for problems involving low-rank matrices, but with complexity bounds that exhibit a hefty polynomial overhead compared to quantum algorithms. This raised the question of whether these methods were actually useful in practice. We conduct a theoretical analysis aimed at identifying their computational bottlenecks, then implement and benchmark the algorithms on a variety of problems, including applications to portfolio optimization and movie recommendations. On the one hand, our analysis reveals that the performance of these algorithms is better than the theoretical complexity bounds would suggest. On the other hand, their performance as seen in our implementation degrades noticeably as the rank and condition number of the input matrix are increased. Overall, our results indicate that quantum-inspired algorithms can perform well in practice provided that stringent conditions are met: low rank, low condition number, and very large dimension of the input matrix. By contrast, practical datasets are often sparse and high-rank, precisely the type that can be handled by quantum algorithms.en_US
dc.language.isoen
dc.publisherVerein zur Forderung des Open Access Publizierens in den Quantenwissenschaftenen_US
dc.relation.isversionof10.22331/Q-2020-08-13-307en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceQuantumen_US
dc.titleQuantum-inspired algorithms in practiceen_US
dc.typeArticleen_US
dc.identifier.citationArrazola, Juan Miguel, Delgado, Alain, Bardhan, Bhaskar Roy and Lloyd, Seth. 2020. "Quantum-inspired algorithms in practice." Quantum, 4.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.relation.journalQuantumen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-01-11T14:39:39Z
dspace.orderedauthorsArrazola, JM; Delgado, A; Bardhan, BR; Lloyd, Sen_US
dspace.date.submission2022-01-11T14:39:45Z
mit.journal.volume4en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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