基于量子纠缠的图灵机优化

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum entanglement for optimizing the fundamental operation of a Turing machine. Traditional Turing machine optimization relies on classical algorithms and heuristics, often struggling with the inherent complexity of the search space. We propose a novel approach utilizing quantum entanglement to simulate the Turing machine's behavior, enabling a fundamentally different optimization paradigm. The core mechanism involves creating entangled qubits that represent the Turing machine's state, allowing for a more efficient exploration of the search space. This research demonstrates a preliminary framework for achieving significant improvements in the efficiency of Turing machine search.

DOI: 10.5281/zenodo.22222590

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