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dc.contributor.authorKrenn, M
dc.contributor.authorKottmann, JS
dc.contributor.authorTischler, N
dc.contributor.authorAspuru-Guzik, A
dc.date.accessioned2021-09-30T03:54:18Z
dc.date.available2021-09-30T03:54:18Z
dc.date.issued2021
dc.identifier.issn2160-3308
dc.identifier.doi10.1103/PhysRevX.11.031044
dc.identifier.urihttp://hdl.handle.net/10072/408477
dc.description.abstractArtificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present Theseus, an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that Theseus will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. Theseus is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level.
dc.description.peerreviewedYes
dc.description.sponsorshipGriffith University
dc.languageen
dc.publisherAmerican Physical Society (APS)
dc.relation.ispartofpagefrom031044
dc.relation.ispartofissue3
dc.relation.ispartofjournalPhysical Review X
dc.relation.ispartofvolume11
dc.subject.fieldofresearchQuantum physics
dc.subject.fieldofresearchCondensed matter physics
dc.subject.fieldofresearchAstronomical sciences
dc.subject.fieldofresearchcode5108
dc.subject.fieldofresearchcode5104
dc.subject.fieldofresearchcode5101
dc.titleConceptual Understanding through Efficient Automated Design of Quantum Optical Experiments
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationKrenn, M; Kottmann, JS; Tischler, N; Aspuru-Guzik, A, Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments, Physical Review X, 2021, 11 (3), pp. 031044
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-09-29T04:42:40Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2021. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
gro.hasfulltextFull Text
gro.griffith.authorTischler, Nora


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