Adaptive Quantum Topology Geometry Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing framework for geometric optimization, leveraging dynamic topology to achieve superior results compared to traditional methods. We propose a quantum algorithm that iteratively adjusts the topology of a geometric object to minimize a defined objective function, effectively exploring a vast space of possible geometries. The core mechanism centers around a quantum-enhanced topology manipulation, allowing for the discovery of complex, non-intuitive geometric solutions. This approach promises to overcome limitations in classical optimization techniques, particularly when dealing with intricate geometric patterns. We present preliminary results demonstrating the framework's effectiveness in optimizing specific geometric shapes, highlighting the potential for broader applicability across diverse fields.

DOI: 10.5281/zenodo.22264275

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