Title: Quantum-Inspired Wave Function Collapse (QWWC) for Machine Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum-Inspired Wave Function Collapse (QWWC) is a novel approach to machine learning algorithm optimization that seeks to enhance model accuracy and generalization through a modified wave function collapse process. Traditional methods often struggle with the inherent randomness of classical algorithms, leading to suboptimal performance. This paper proposes a new method that introduces quantum-inspired noise and entanglement into the wave function collapse, aiming to mitigate these limitations and improve model reliability. We explore the potential benefits of this approach in various machine learning tasks, focusing on [Specific Machine Learning Task - e.g., Deep Neural Network Classification] and demonstrating its effectiveness through simulations and preliminary experiments. The core of this work lies in the design of a novel noise distribution and entanglement strategy that minimizes the effects of classical randomness while maximizing the impact of quantum-inspired effects. This research highlights the potential for a paradigm shift in machine learning optimization, moving beyond approximations towards a more robust and precise approach.

DOI: 10.5281/zenodo.22296839

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