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dc.contributor.authorRalston, John P.
dc.date.accessioned2023-07-31T16:31:42Z
dc.date.available2023-07-31T16:31:42Z
dc.date.issued2022-11-12
dc.identifier.citationRalston, J.P. What Can We Learn from Entanglement and Quantum Tomography? Physics 2022, 4, 1371-1383. https://doi.org/10.3390/physics4040088en_US
dc.identifier.urihttps://hdl.handle.net/1808/34672
dc.description.abstractEntanglement has become a hot topic in nuclear and particle physics, although many physicists are not sure they know what it means. We maintain that an era of understanding and using quantum mechanics on a dramatically new basis has arrived. We review a viewpoint that treats the subject as being primarily descriptive and completely free of the intellectual straitjackets and mysticism argued over long ago. Quantum probability is an extension of classical probability, but with universal uses. Density matrices describe systems where entanglement or its absence is a classification tool. Most of these have been known for decades, but there is a new way of understanding them that is liberated from the narrow outlook of the early days.en_US
dc.publisherMDPIen_US
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectData analysisen_US
dc.subjectQuantum tomographyen_US
dc.subjectEntanglementen_US
dc.subjectFactorizationen_US
dc.titleWhat Can We Learn from Entanglement and Quantum Tomography?en_US
dc.typeArticleen_US
kusw.kuauthorRalston, John P.
kusw.kudepartmentPhysics & Astronomyen_US
dc.identifier.doi10.3390/physics4040088en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-9296-6018en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccessen_US


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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.