自适应拓扑的量子神经网络加速器

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel quantum neural network (QNN) accelerator leveraging adaptive topology to optimize the training and inference processes of neural networks. Traditional quantum computation methods often require intricate hardware setups, limiting scalability and efficiency. This research proposes a system that utilizes quantum entanglement to dynamically adjust the network's topology, leading to substantial improvements in both training and inference speed. We demonstrate the efficacy of this approach through simulations and preliminary experimental results, showcasing a significant reduction in computational complexity compared to conventional methods. The core mechanism centers on exploiting the inherent properties of quantum entanglement to create a flexible and optimized network architecture.

DOI: 10.5281/zenodo.22322887

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