Quantum Geometry Optimization: Leveraging Entanglement for Accelerated Complex Shape Design

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of quantum entanglement to revolutionize complex geometric optimization problems. We propose a novel non-linear geometric optimization algorithm, 'Entangled Resonance Optimization' (ERO), that harnesses the unique properties of quantum entanglement to accelerate the search for optimal geometric configurations. The core mechanism centers around leveraging entangled qubits to explore the solution space more effectively, enabling the efficient discovery of intricate shapes and designs. This work addresses the limitations of traditional optimization techniques, particularly in scenarios requiring complex geometries and high-dimensional spaces, offering a fundamentally different approach to geometric problem-solving. The paper details the theoretical framework, algorithm implementation, and preliminary experimental results demonstrating the effectiveness of ERO in optimizing various geometric shapes. We conclude with a discussion of future research directions, including exploring the scalability of the quantum approach and extending it to more complex optimization tasks.

DOI: 10.5281/zenodo.22263794

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