Quantum-Enhanced Geometric Representation for Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Enhanced Geometric Representation for Optimization explores the potential of quantum entanglement to revolutionize optimization algorithms. Current optimization methods often struggle with the complexity of high-dimensional spaces and the speed of convergence. This paper proposes a novel approach leveraging quantum entanglement to represent geometric parameters, fostering instantaneous exploration of the solution space and significantly improving the speed and accuracy of solution finding. We demonstrate the effectiveness of this method through theoretical analysis and preliminary simulations, offering a fundamentally new pathway for optimization problems.

DOI: 10.5281/zenodo.22263244

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