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dc.contributor.authorKiani, Bobak Toussi
dc.contributor.authorDe Palma, Giacomo
dc.contributor.authorEnglund, Dirk
dc.contributor.authorKaminsky, William
dc.contributor.authorMarvian, Milad
dc.contributor.authorLloyd, Seth
dc.date.accessioned2024-03-26T19:34:12Z
dc.date.available2024-03-26T19:34:12Z
dc.date.issued2022-02-14
dc.identifier.issn2469-9926
dc.identifier.issn2469-9934
dc.identifier.urihttps://hdl.handle.net/1721.1/153943
dc.description.abstractQuantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.en_US
dc.language.isoen
dc.publisherAmerican Physical Societyen_US
dc.relation.isversionof10.1103/physreva.105.022415en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAmerican Physical Societyen_US
dc.titleQuantum advantage for differential equation analysisen_US
dc.typeArticleen_US
dc.identifier.citationKiani, Bobak Toussi, De Palma, Giacomo, Englund, Dirk, Kaminsky, William, Marvian, Milad et al. 2022. "Quantum advantage for differential equation analysis." Physical Review A, 105 (2).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronics
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.relation.journalPhysical Review Aen_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.updated2024-03-26T19:20:46Z
dspace.orderedauthorsKiani, BT; De Palma, G; Englund, D; Kaminsky, W; Marvian, M; Lloyd, Sen_US
dspace.date.submission2024-03-26T19:20:48Z
mit.journal.volume105en_US
mit.journal.issue2en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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