Title: Quantum-Inspired Iterative Refinement of Geometric Models

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired iterative refinement of geometric models aims to improve model accuracy through a novel approach that mimics quantum mechanics. This research introduces a quantum-inspired algorithm leveraging entanglement and superposition to efficiently explore parameter spaces and enhance model performance. The core mechanism involves iteratively refining a geometric model by exploring subtle variations in its parameters, guided by the quantum-inspired optimization techniques, ultimately leading to improved accuracy and robustness. The study demonstrates the potential for significant acceleration in model training and refinement, offering a promising avenue for advancing machine learning applications requiring high-fidelity geometric representation.

DOI: 10.5281/zenodo.22199770

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