Quantum Entanglement Field Theory: A Probabilistic Algorithm for Optimization and Machine Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic algorithm based on Quantum Entanglement Field Theory (QETF) designed to address optimization problems and machine learning tasks. The core mechanism leverages the unique properties of quantum entanglement to enhance computational efficiency and precision. We propose a model that dynamically adjusts quantum state representations to achieve more accurate optimization and efficiently handle high-dimensional problems. The paper explores the potential of QETF to revolutionize these fields by offering a fundamentally different approach to problem-solving. This work demonstrates the feasibility of utilizing entanglement to improve algorithm performance, particularly in situations involving complex optimization challenges.

DOI: 10.5281/zenodo.22184620

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