Core Claim: A quantum entanglement-based adaptive topology optimization method utilizes the unique properties of quantum entanglement to achieve topology-based optimization and transformation, applying to gene sequencing optimization and data compression.

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum entanglement offers a novel approach to optimization problems, potentially surpassing classical algorithms. This paper introduces a quantum entanglement-based adaptive topology optimization method designed to enhance data compression and gene sequence optimization. We explore the utilization of entanglement to dynamically manipulate topological structures, allowing for more efficient transformations and a deeper understanding of data representation. The method employs a quantum circuit to iteratively refine a topological map, leveraging the correlations inherent in entangled states. The core mechanism focuses on creating and manipulating entanglement to achieve a form of topology-driven transformation, offering a potential advancement in optimization techniques. This work demonstrates the feasibility of utilizing entanglement for optimization through a novel approach, with preliminary results showing promising improvements in data compression and gene sequence representation. The resulting framework presents a new avenue for exploring optimization problems within the context of quantum mechanics.

DOI: 10.5281/zenodo.22156016

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