Quantum Probability Models and Machine Learning: A Collaborative Optimization Approach

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum probability models and machine learning represent a synergistic approach with the potential to significantly enhance the capabilities of both fields. This paper explores a novel method for collaborative optimization, leveraging the unique strengths of quantum computation to accelerate the training and optimization of machine learning models. We present a framework that integrates quantum probability models to enhance feature representation, optimize model parameters, and improve generalization, ultimately leading to improved performance across a range of machine learning tasks. The core mechanism focuses on utilizing quantum algorithms to efficiently explore the parameter space, minimizing computational complexity and enabling the training of models with increased capacity. The approach addresses key challenges in machine learning, particularly in high-dimensional spaces and complex parameter spaces, presenting a promising pathway toward more effective and scalable models.

DOI: 10.5281/zenodo.22332160

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