dc.creatorNieto-Chaupis, Huber
dc.date.accessioned2022-04-29T14:38:01Z
dc.date.available2022-04-29T14:38:01Z
dc.date.created2022-04-29T14:38:01Z
dc.date.issued2021-12
dc.identifierNieto-Chaupis, H. (2021). The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 135-136). IEEE.
dc.identifier978-1-6654-3736-3
dc.identifierhttps://hdl.handle.net/20.500.13067/1807
dc.identifier2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
dc.identifierhttps://doi.org/10.1109/AIKE52691.2021.00029
dc.description.abstractThe role of evolution operator is to provide the time displacement of wave function through the Hamiltonian of the system. The usage of coordinates representation gives the well-known propagator that is the Green’s function. In this paper it is emphasized that once the propagator is projected onto a scenario of machine learning it would acquire the role of performance in according to the criteria of Tom Mitchell. In this manner from the resulting wave function the probability is simulated presenting noteworthy morphologies in the which the system displays high values of probability for the measurement of distances.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisherPE
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127625788&doi=10.1109%2fAIKE52691.2021.00029&partnerID=40&md5
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceAUTONOMA
dc.source135
dc.source136
dc.subjectKnowledge engineering
dc.subjectConferences
dc.subjectQuantum mechanics
dc.subjectMorphology
dc.subjectMachine learning
dc.subjectWave functions
dc.titleThe Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
dc.typeinfo:eu-repo/semantics/article


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