Quantum Information Space: A Novel Non-Linear Geometric Optimization Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum information space offers a paradigm shift in algorithmic design, particularly when dealing with complex geometric optimization problems, especially those involving high-dimensional non-linear geometric shapes. This paper explores the design and potential applications of a novel optimization algorithm based on quantum information space, leveraging quantum entanglement for enhanced global search. We present a core claim asserting the efficacy of this approach in addressing challenges faced by traditional optimization techniques. The core mechanism involves mapping geometric shapes to quantum states, utilizing entanglement for global exploration, and demonstrating its potential to outperform existing methods in specific, challenging scenarios. This research investigates the feasibility of this new algorithm and its implications for various fields, including computational geometry and materials science.

DOI: 10.5281/zenodo.22609106

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