The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements

• Autor: Nieto-Chaupis, Huber
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/1807
• Enlace del recurso: https://hdl.handle.net/20.500.13067/1807; https://doi.org/10.1109/AIKE52691.2021.00029
• Nivel de acceso: acceso restringido
• Materia: Knowledge engineering; Conferences; Quantum mechanics; Morphology; Machine learning; Wave functions; https://purl.org/pe-repo/ocde/ford#2.02.04

The 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.