Implementable hybrid quantum ant colony optimization algorithm

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dc.contributor.authorde Andoin, M. Garcia
dc.contributor.authorEchanobe, J.
dc.date.accessioned2022-07-29T11:31:06Z
dc.date.available2022-07-29T11:31:06Z
dc.date.issued2022-06-17
dc.description.abstractWe propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to produce approximate solutions for NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codifies only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithmen
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This received support from Tecnalia and the University of the Basque Country (UPV-EHU) 2021 PIF contract call. Mikel Garcia de Andoin acknowledges funding from the QUANTEK project (ELKARTEK program from the Basque Government, expedient no. KK-2021/00070).en
dc.identifier.citationde Andoin, M. Garcia, and J. Echanobe. “Implementable Hybrid Quantum Ant Colony Optimization Algorithm.” Quantum Machine Intelligence 4, no. 2 (2022). https://doi.org/10.1007/s42484-022-00065-1.en
dc.identifier.doi10.1007/s42484-022-00065-1en
dc.identifier.essn2524-4914en
dc.identifier.issn2524-4906en
dc.identifier.urihttp://hdl.handle.net/11556/1392
dc.issue.number2en
dc.journal.titleQuantum Machine Intelligenceen
dc.language.isoengen
dc.publisherSpringer Science and Business Media Deutschland GmbHen
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.keywordsQuantum computingen
dc.subject.keywordsHybrid quantum algorithmen
dc.subject.keywordsQuantum ant colony optimizationen
dc.subject.keywordsAnt colony optimizationen
dc.subject.keywordsQuadratic assignment problemen
dc.titleImplementable hybrid quantum ant colony optimization algorithmen
dc.typejournal articleen
dc.volume.number4en
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