论文标题
使用跨渠道池量量子拆分神经网络学习
Quantum Split Neural Network Learning using Cross-Channel Pooling
论文作者
论文摘要
近年来，量子科学领域引起了各种学科的重大兴趣，包括量子机学习，量子通信和量子计算。在这些新兴领域中，由于量子神经网络（QNN）与传统联邦学习（FL）技术的整合，量子联合学习（QFL）引起了人们的特别关注。在这项研究中，提出了一种名为“量子拆分学习”（QSL）的新方法，这代表了经典分裂学习的高级扩展。古典计算的先前研究表明，分裂学习的许多优势，例如加速融合，降低通信成本和增强的隐私保护。为了最大程度地提高QSL的潜力，引入了跨渠道合并，该技术利用了QNNS促进的量子状态断层扫描的独特性能。通过严格的数值分析，有证据表明，与QFL相比，QSL不仅可以达到1.64 \％的TOP-1准确性，而且在MNIST分类任务的背景下证明了坚固的隐私保护。
In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing. Among these emerging areas, quantum federated learning (QFL) has gained particular attention due to the integration of quantum neural networks (QNNs) with traditional federated learning (FL) techniques. In this study, a novel approach entitled quantum split learning (QSL) is presented, which represents an advanced extension of classical split learning. Previous research in classical computing has demonstrated numerous advantages of split learning, such as accelerated convergence, reduced communication costs, and enhanced privacy protection. To maximize the potential of QSL, cross-channel pooling is introduced, a technique that capitalizes on the distinctive properties of quantum state tomography facilitated by QNNs. Through rigorous numerical analysis, evidence is provided that QSL not only achieves a 1.64\% higher top-1 accuracy compared to QFL but also demonstrates robust privacy preservation in the context of the MNIST classification task.
