Machine Learning as Mediator of Classical Electrodynamics and Quantum Mechanics

Abstract

Machine Learning has been used as part of a closed-form operation that allows to link classical physics to quantum mechanics of a free electron radiating photons inside superintese laser field. To accomplish this an engineered perceptron has been constructed. Thus, the usage of integer-order Bessel functions would play a relevant role. The criterion suggested by Ritus and Nikishov have could to end at the construction of a model that allows to estimate quantized energy. The proposal perceptron might be an effective generator of quantum mechanics observables depending on weights as well as the specific operation of activation. From the results, one would arrive to suggest the idea Machine Learning might to play the role as an effective constructor of quantum theories without any knowledge of it, previously.