联邦量子与经典计算：一种隐私保护的混合方法
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
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中文总结 AI 辅助
本研究提出用联邦学习结合盲垂直联邦学习协议，实现隐私保护的量子-经典混合模型协作，在SMPP基准上显著提升准确率并减少通信与参数。
中文摘要 AI 辅助
量子机器学习（QML）日益被认为是量子计算最有前景的近端应用之一，被视为超越纯经典方法的下一代前沿候选。混合量子-经典模型通过将参数化量子电路嵌入到所有其他组件均为经典的模型中，实现了这一潜力——这种设计已应用于化学模拟、金融建模和图像分类。然而，它们在隐私敏感的多方场景中的部署受到限制，原因在于需要避免集中原始数据，以及现代量子电路必须保持参数高效以在规模上保持可训练性。在本文中，我们通过评估联邦学习（FL）作为结合混合量子-经典主动方与经典被动方的手段来解决这些限制，使用此http URL的盲垂直联邦学习（SBVFL）协议来避免集中原始数据，同时大幅减少通信。我们遵循常见的QML设计实践，构建了分裂乘法周期奇偶校验（SMPP）基准。在此任务上，我们的模拟表明，与本地训练相比，SBVFL将准确率从0.7227提高到0.8757，接近非私有集中式准确率，并且混合量子-经典模型以远少于经典神经网络和随机森林备选方案的可训练参数实现了这一性能。这些结果表明，联邦学习能够在无需集中原始数据的情况下，实现高性能、隐私保护的量子-经典协作。
英文摘要
Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purely classical approaches. Hybrid quantum-classical models operationalize this potential by embedding a parameterized quantum circuit within a model where all other components remain classical-a design already applied to chemistry simulation, financial modeling, and image classification. However, their deployment in privacy-sensitive, multi-party settings is constrained by the need to avoid centralizing raw data and by the requirement that modern quantum circuits remain parameter-efficient to stay trainable at scale. In this paper, we address these constraints by evaluating federated learning (FL) as a means of combining a hybrid quantum-classical active party with a classical passive party, using Sherpa.ai's Blind Vertical FL (SBVFL) protocol to avoid centralizing raw data, while drastically reducing communication. We construct the split multiplicative periodic parity (SMPP) benchmark, following common QML design practice. On this task, our simulations show that SBVFL raises accuracy from 0.7227 to 0.8757 compared to local training, closely approaching non-private centralized accuracy, and that the hybrid quantum-classical model achieves this with substantially fewer trainable parameters than the classical neural networks and random forest alternatives. These results show that FL enables high-performing, privacy-preserving quantum-classical collaboration without centralizing raw data.
