基于量子概率模型的超深层神经网络

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum probabilistic models to enhance the efficiency and expressiveness of deep neural networks. Traditional deep learning architectures often struggle with high-dimensional data, necessitating computationally intensive operations. We propose a novel algorithm that integrates quantum probability models into the core computation process of neural networks. This integration leverages the inherent advantages of quantum mechanics, particularly superposition and entanglement, to improve representation learning and parameter estimation. The core mechanism involves mapping the neural network's activation functions and gradient computations into quantum states, allowing for parallel processing and potentially accelerating training and inference. We analyze the theoretical and practical implications of this approach, demonstrating its potential to overcome limitations associated with classical neural networks in specific scenarios. The research emphasizes the benefits of quantum-enhanced representations and efficient parameter estimation.

DOI: 10.5281/zenodo.22265923

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