Nonlinear Geometry-Driven Quantum Computing Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel quantum computing optimization algorithm based on the integration of non-linear geometry functions. The core concept revolves around modeling quantum computation processes as non-linear geometric functions, allowing for automated adjustments to optimize quantum circuit parameters and overall system performance. We demonstrate the effectiveness of this approach through a detailed analysis and implementation of a specific optimization framework. The system's design prioritizes enhanced optimization efficiency and reduced reliance on manual parameter tuning, offering a significant advancement in quantum computing algorithm development.

DOI: 10.5281/zenodo.22184922

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