Quantum-Based Probabilistic Model Learning

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the potential of quantum machine learning (QML) for efficient and accurate probabilistic model learning. Traditional machine learning algorithms often struggle with computational complexity and scalability, particularly when dealing with high-dimensional data and complex probability distributions. This research proposes a novel approach leveraging principles from quantum information theory to design a QML algorithm capable of learning probabilistic models. The core mechanism involves representing model parameters as quantum states and utilizing quantum measurements and entanglement to perform parameter estimation. We aim to demonstrate that this quantum-based method can overcome limitations inherent in classical approaches, potentially leading to significant improvements in both computational efficiency and predictive accuracy. This work presents a foundational framework for further research and development in this emerging field.

DOI: 10.5281/zenodo.22157647

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