Core Claim: Leveraging quantum entanglement, we design a geometric optimization algorithm based on adaptive topology transformations, dynamically adjusting transformation parameters based on input data and computation process, resulting in more efficient optimization.

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers a unique paradigm for computation, and its potential applications extend beyond quantum computing to various fields, including optimization. This paper investigates the utilization of quantum entanglement within a geometric optimization framework, exploring the design of an adaptive topology transformation algorithm. The core mechanism centers around harnessing the correlations inherent in entangled states to dynamically adjust transformation parameters during the optimization process. We propose an algorithm that leverages the properties of entanglement to achieve higher efficiency and precision compared to traditional optimization techniques. The study focuses on a novel approach to parameterization and the resulting impact on the optimization process. This work presents a fundamental design for a quantum-enhanced geometric optimization strategy, demonstrating the potential of entanglement to significantly improve optimization outcomes.

DOI: 10.5281/zenodo.22192738

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