Title: Inverse-Quantum-Classical Convergence (IQCC) for Machine Learning

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

The convergence of machine learning models is a significant challenge, often requiring extensive computational resources and prolonged training times. Traditional methods often struggle to achieve optimal performance within a reasonable timeframe. This paper proposes Inverse-Quantum-Classical Convergence (IQCC) as a novel approach to address this bottleneck, leveraging the synergistic potential of quantum and classical computational resources during training. We develop a dynamic weighting mechanism that intelligently orchestrates the contribution of each computational paradigm, resulting in significantly faster convergence and improved model performance. This method offers a promising pathway towards more efficient and scalable machine learning.

DOI: 10.5281/zenodo.22462156

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