Probability Logic Quantum Algorithm

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic quantum algorithm designed to tackle complex probability modeling problems, including machine learning model training and optimization, and high-precision probability inference. We propose a framework that leverages the core concepts of probability logic – probability distributions, Bayesian inference – and quantum computing's unique properties like superposition and entanglement. The algorithm's efficiency is achieved through optimized quantum circuits and a carefully designed quantum-classical hybrid approach. We demonstrate the algorithm's effectiveness through a series of illustrative examples, showcasing its potential to significantly accelerate these computationally intensive tasks. This work presents a new approach to quantum computation that prioritizes logical reasoning and efficient probability manipulation, offering a pathway toward advanced statistical modeling and decision-making.

DOI: 10.5281/zenodo.22267962

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