Quantum Entanglement-Based Turing Machine Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre yet fundamental phenomenon in quantum mechanics, holds the potential for revolutionary advancements in computation. This research explores the application of entanglement to optimize Turing machines, a foundational model in computer science. The core claim is to develop a novel quantum optimization algorithm that employs entanglement to enhance the efficiency and performance of Turing machines, specifically by facilitating faster exploration of the search space. The paper details the methodology, explores the theoretical implications, and presents preliminary results demonstrating the potential benefits of this approach. We argue that exploiting entanglement can provide a significant advantage over traditional methods, particularly in complex optimization problems.

DOI: 10.5281/zenodo.22281311

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