dc.creatorNieto-Chaupis, Huber
dc.date.accessioned2023-12-28T14:33:08Z
dc.date.accessioned2024-08-06T20:56:12Z
dc.date.available2023-12-28T14:33:08Z
dc.date.available2024-08-06T20:56:12Z
dc.date.created2023-12-28T14:33:08Z
dc.date.issued2023
dc.identifierhttps://hdl.handle.net/20.500.13067/2923
dc.identifierIntelligent Systems and Applications
dc.identifierhttps://doi.org/10.1007/978-3-031-16072-1_6
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9538999
dc.description.abstractIn Physics the energy of any system represents a sensitive variable because of it depends the functionality and evolution of system at time. Thus the deep knowledge of the interactions of system might be a remarkable advantage as to anticipate stochastic fluctuations as well as minimize the errors at the done measurements. Thus, in this paper a particular attention is paid on the mathematical characteristics of the quantum mechanics evolution operator when it is projected onto a full scenario of principles based at Machine Learning. In concrete the case of pass of charged particle through a bunch of charged particles can be perceived as a system exhibiting oscillations because the attraction and repulsion forces experienced along the space-time trajectory. The fact that the energy can be controllable by using free parameters can be advantageous in the sense of providing a learning to the system in order to optimize the total energy at key space-time coordinates.
dc.languageeng
dc.publisherSpringer Link
dc.relationhttps://link.springer.com/chapter/10.1007/978-3-031-16072-1_6
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.source82
dc.source96
dc.subjectQuantum mechanics
dc.subjectMachine learning
dc.subjectTom MItchell
dc.titleQuantum Displacements Dictated by Machine Learning Principles: Towards Optimization of Quantum Paths
dc.typeinfo:eu-repo/semantics/article


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