自适应量子拓扑熵的优化算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum topology, a rapidly developing field, offers a fundamentally new approach to quantum computation, leveraging the unique properties of spacetime topology. One promising application is the manipulation of quantum states using quantum topology, and a significant challenge lies in efficiently optimizing these manipulations. This paper investigates a novel optimization algorithm designed to address this challenge by learning the entropy of quantum topological states and dynamically adjusting quantum states based on this entropy gradient. We propose a method that utilizes a learned representation of the quantum state's entropy as a guiding principle for state optimization, aiming to enhance the performance of quantum computations. This work presents a framework for optimizing quantum state manipulation, potentially offering a significant advancement in the efficiency of quantum algorithms. The core mechanism involves learning and utilizing the entropy of quantum topological states to guide dynamic state adjustments. The research explores the potential of this approach to improve the scalability and effectiveness of quantum computations.

DOI: 10.5281/zenodo.22495029

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