联邦量子训练与批量参数生成
Federated Quantum-Train with Batched Parameter Generation
AI总结：
本文提出联邦量子训练框架，通过量子神经网络与映射模型在客户端生成参数并在中心节点聚合，在CIFAR-10上以类VGG网络验证，将量子比特用量从19降至8并降低泛化误差，且推理无需量子计算机。
摘要：
在这项工作中，我们提出了联邦量子训练（QT）框架，该框架将QT模型集成到联邦学习中，以利用量子计算来构建分布式学习系统。量子客户端节点采用量子神经网络（QNNs）和一个映射模型来生成局部目标模型参数，这些参数在中心节点进行更新和聚合。我们在CIFAR-10数据集上使用类VGG卷积神经网络进行测试，结果表明我们的方法将量子比特使用量从19个显著降低到最低8个，同时降低了泛化误差。QT方法缓解了经典模型中观察到的过拟合现象，使训练准确率和测试准确率趋于一致，并提升了高度压缩模型的性能。值得注意的是，联邦QT框架在推理阶段不需要量子计算机，这在当前量子硬件受限的情况下增强了实用性。这项工作凸显了将量子技术集成到联邦学习中的潜力，为量子机器学习和分布式学习系统的发展铺平了道路。
英文摘要：
In this work, we introduce the Federated Quantum-Train (QT) framework, which integrates the QT model into federated learning to leverage quantum computing for distributed learning systems. Quantum client nodes employ Quantum Neural Networks (QNNs) and a mapping model to generate local target model parameters, which are updated and aggregated at a central node. Testing with a VGG-like convolutional neural network on the CIFAR-10 dataset, our approach significantly reduces qubit usage from 19 to as low as 8 qubits while reducing generalization error. The QT method mitigates overfitting observed in classical models, aligning training and testing accuracy and improving performance in highly compressed models. Notably, the Federated QT framework does not require a quantum computer during inference, enhancing practicality given current quantum hardware limitations. This work highlights the potential of integrating quantum techniques into federated learning, paving the way for advancements in quantum machine learning and distributed learning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
