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Theory of machine learning based on nonrelativistic quantum mechanics
(World Scientific, 2021)
The goal of this paper is the presentation of the elementary procedures that normally are done in nonrelativistic Quantum Mechanics in terms of the principles of Machine Learning. In essence, this paper discusses Mitchell's ...
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The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
(Institute of Electrical and Electronics Engineers, 2021-12)
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. ...
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Quantum Displacements Dictated by Machine Learning Principles: Towards Optimization of Quantum Paths
(Springer Link, 2023)
In 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 ...
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The Criteria of Mitchell to Interpret Classical Radiation as Compton Scattering
(IEEE, 2022)
The principles of Machine Learning through the criteria of Mitchell are used to validate a concrete quantum-mechanics interpretation from a classical radiation scheme inside the framework of linear and nonlinear Compton ...
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The Machine Learning Principles Based at the Quantum Mechanics Postulates
(Springer Link, 2022)
Quantum mechanics is governed by well-defined postulates by the which one can go through either theory or experimental studies in order to perform measurements of microscopic dynamics of elementary particles, atoms and ...
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Quantum Mechanics of Theorem of Bayes Modeled by Machine Learning Principles
(IEEE, 2022)
A theory consisting in quantum mechanics and theorem of Bayes, is presented. In essence, the Bayes probability has been built from two subspaces. While in one some quantum measurements are done, in the another it is seen ...
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