Wave Function Collapse for Quantum Machine Learning

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum machine learning (QML) holds immense promise for revolutionizing various fields, including artificial intelligence and data analysis. A fundamental challenge in QML lies in efficiently exploring complex solution spaces, especially during optimization processes. This paper investigates the application of wave function collapse, a cornerstone of quantum mechanics, as a potential mechanism to accelerate these exploration processes. We explore the feasibility of incorporating this concept into an algorithm designed for optimization tasks within quantum machine learning, aiming to reduce the computational burden and enhance the speed of training and inference. The core of our work centers on demonstrating the potential of wave function collapse to guide the algorithm towards optimal solutions through a novel, iterative process. This paper presents a theoretical framework and preliminary implementation illustrating the potential of this approach.

DOI: 10.5281/zenodo.22220423

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