Quantum Machine Learning With Canonical Variables

Fuente: arXiv
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Autore principale: Fuentes, Jesús
Natura: Preprint
Pubblicazione: 2024
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author Fuentes, Jesús
author_facet Fuentes, Jesús
contents Utilising dynamic electromagnetic field control over charged particles serves as the basis for a quantum machine learning platform that operates on observables rather than directly on states. Such a platform can be physically realised in ion traps or particle confinement devices that utilise electromagnetic fields as the source of control. The electromagnetic field acts as the ansatz within the learning algorithm. The models discussed are exactly solvable, with exact solutions serving as precursors for learning tasks to emerge, including regression and classification algorithms as particular cases. This approach is considered in terms of canonical variables with semi-classical behaviour, disregarding relativistic degrees of freedom.
format Preprint
id arxiv_https___arxiv_org_abs_2406_06666
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Quantum Machine Learning With Canonical Variables
Fuentes, Jesús
Quantum Physics
Utilising dynamic electromagnetic field control over charged particles serves as the basis for a quantum machine learning platform that operates on observables rather than directly on states. Such a platform can be physically realised in ion traps or particle confinement devices that utilise electromagnetic fields as the source of control. The electromagnetic field acts as the ansatz within the learning algorithm. The models discussed are exactly solvable, with exact solutions serving as precursors for learning tasks to emerge, including regression and classification algorithms as particular cases. This approach is considered in terms of canonical variables with semi-classical behaviour, disregarding relativistic degrees of freedom.
title Quantum Machine Learning With Canonical Variables
topic Quantum Physics
url https://arxiv.org/abs/2406.06666