基于自适应图灵机的量子态优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum state optimization algorithm based on an adaptive Turing machine. Traditional quantum state optimization methods often employ static parameter adjustments, limiting their ability to adapt to complex quantum systems. This paper proposes a dynamic adjustment mechanism utilizing an adaptive Turing machine, enabling the algorithm to dynamically adjust quantum state parameters to achieve higher precision and adaptability. We present a rigorous theoretical analysis and demonstrate its effectiveness through simulations. The core mechanism involves defining a set of adaptive rules based on quantum state characteristics, allowing the algorithm to iteratively refine the state to minimize an objective function. This results in improved performance across a range of quantum measurement tasks.

DOI: 10.5281/zenodo.22267355

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