Hamiltonian-based Quantum Paths Guided by Engineered Perceptron

The case of a massive particle traveling along the space-plane but guided by an algorithm based at perceptron, is presented. Essentially, it is defined the corresponding Hamiltonian that in a first instance turns out to be for free particle. However, the presence of disturbs suggest to employ additional terms to Hamiltonian in according to perceptron topology. In this manner, the activation function is focused on the expended energy from a point to next. The space-time transitions are canceled when disturbs are big enough that particle's trajectory if fully disturbed so that probability that final point is reached is small. Only when perceptron is adequate to every space-point, algorithm can guarantee the arrival of particle for all available quantum states.

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Published
2024-12-27
DOI
https://doi.org/10.1109/iceet65156.2024.10913682
Primary Topic
Neural Networks and Applications
Type
article
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article

Hamiltonian-based Quantum Paths Guided by Engineered Perceptron

Huber Nieto–Chaupis
Neural Networks and Applications
article

Hamiltonian-based Quantum Paths Guided by Engineered Perceptron

Huber Nieto–Chaupis
article en

Abstract

The case of a massive particle traveling along the space-plane but guided by an algorithm based at perceptron, is presented. Essentially, it is defined the corresponding Hamiltonian that in a first instance turns out to be for free particle. However, the presence of disturbs suggest to employ additional terms to Hamiltonian in according to perceptron topology. In this manner, the activation function is focused on the expended energy from a point to next. The space-time transitions are canceled when disturbs are big enough that particle's trajectory if fully disturbed so that probability that final point is reached is small. Only when perceptron is adequate to every space-point, algorithm can guarantee the arrival of particle for all available quantum states.

Universidad Panamericana (GT)
Affordable and clean energy
Openalex Percentile: Top 74%
Neural Networks and Applications
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