Quantum Machine Learning With Canonical Variables

• URL: http://arxiv.org/abs/2406.06666v1
• Date: Mon, 10 Jun 2024 16:10:42 GMT
• Title: Quantum Machine Learning With Canonical Variables
• Authors: Jesús Fuentes,
• Abstract summary: 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. The platform can be physically realised in ion traps or particle confinement devices that utilise electromagnetic fields as the source of control.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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