QUANTUM-ASSISTED GREEDY ALGORITHMS

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dc.contributor.authorAyanzadeh, Ramin
dc.contributor.authorDorband, John E
dc.contributor.authorHalem, Milton
dc.contributor.authorFinin, Tim
dc.date.accessioned2022-08-18T22:27:38Z
dc.date.available2022-08-18T22:27:38Z
dc.date.issued2022-09-28
dc.descriptionProceedings of the International Geoscience and Remote Sensing Symposium (IGARSS)en_US
dc.description.abstractWe show how to leverage quantum annealers (QAs) to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making locally optimal choices at each stage, we use QAs that sample from the ground state of a problem-dependent Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables. More specifically, we look at each spin of the Ising model as a random variable and contract all problem variables whose corresponding uncertainties are negligible. Our empirical results on a D-Wave 2000Q quantum processor demonstrate that the proposed quantum-assisted greedy algorithm (QAGA) scheme can find notably better solutions compared to the state-of-the-art techniques in the realm of quantum annealing.en_US
dc.description.sponsorshipThis research was supported by NASA grant (#NNH16ZDA001N-AIST16-0091), NIH-NIGMS Initiative for Maximizing Student Development Grant (2 R25-GM55036), and a Google Lime scholarship. We thank the D-Wave Systems management team, namely Rene Copeland, for granting us access to the D-Wave 2000Q quantum annealer.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9884795en_US
dc.format.extent4 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepresentations (communicative events)en_US
dc.genrepreprints
dc.identifierdoi:10.13016/m2rrje-bheo
dc.identifier.citationR. Ayanzadeh, J. Dorband, M. Halem and T. Finin, "Quantum-Assisted Greedy Algorithms," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 4911-4914, doi: 10.1109/IGARSS46834.2022.9884795.
dc.identifier.urihttp://hdl.handle.net/11603/25494
dc.identifier.urihttps://doi.org/10.1109/IGARSS46834.2022.9884795
dc.language.isoen_USen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rights© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleQUANTUM-ASSISTED GREEDY ALGORITHMSen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0001-6687-5668en_US
dcterms.creatorhttps://orcid.org/0000-0002-6593-1792en_US

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