基于自适应量子计算的概率论推理框架

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic reasoning framework leveraging quantum entanglement and superposition to enhance computational capabilities. Traditional probabilistic reasoning methods are inherently limited by classical computational resources. This work proposes a dynamically adaptive quantum computing architecture designed to exploit these quantum properties, offering a paradigm shift towards more efficient and powerful probabilistic inference. The framework utilizes quantum entanglement to establish correlated states, enabling the exploration of complex logical relationships. We detail the core mechanism, including the quantum state initialization, entanglement generation, and logical operation sequences. The paper presents a preliminary evaluation demonstrating the framework's potential for improved performance in specific probabilistic reasoning tasks. The core claim is that this framework will significantly improve computational efficiency and accuracy compared to existing approaches.

DOI: 10.5281/zenodo.22198741

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