Title: Quantum-Inspired Non-Linearity in Machine Learning

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-inspired non-linearities offer a promising avenue for enhancing the robustness and generalization of machine learning models. This paper explores the potential of incorporating quantum-like phenomena into learning algorithms to mitigate noise and facilitate adaptation. We propose a novel framework that leverages superposition and entanglement to create models less susceptible to fluctuations in input data, ultimately improving predictive performance. The core mechanism focuses on introducing these quantum-inspired elements to promote stability and resilience in model training and inference. The research aims to address limitations of current machine learning approaches by introducing a fundamentally different method for handling uncertainty and exploring robust solutions. We present preliminary results demonstrating the effectiveness of this framework in several benchmark datasets, highlighting its potential for advancing the field.

DOI: 10.5281/zenodo.22502154

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