Quantum Machine Learning (QML) is an emerging field of research with potential applications to distributed collaborative learning, such as Split Learning (SL). SL allows resource-constrained clients to collaboratively train ML models with a server, reduce their computational overhead, and enable data privacy by avoiding raw data sharing. Although QML with SL has been studied, the problem remains open in resource-constrained environments where clients lack quantum computing capabilities. Additionally, data privacy leakage between client and server in SL poses risks of reconstruction attacks on the server side. To address these issues, we propose Hybrid Quantum Split Learning (HQSL), an application of Hybrid QML in SL. HQSL enables classical clients to train models with a hybrid quantum server and curtails reconstruction attacks. In addition, we introduce a novel qubit-efficient data-loading technique for designing a quantum layer in HQSL, minimizing both the number of qubits and circuit depth. Experiments on five datasets demonstrate HQSL's feasibility and ability to enhance classification performance compared to its classical models. Notably, HQSL achieves mean improvements of over 3% in both accuracy and F1-score for the Fashion-MNIST dataset, and over 1.5% in both metrics for the Speech Commands dataset. We expand these studies to include up to 100 clients, confirming HQSL's scalability. Moreover, we introduce a noise-based defense mechanism to tackle reconstruction attacks on the server side. Overall, HQSL enables classical clients to collaboratively train their models with a hybrid quantum server, leveraging quantum advantages while improving model performance and security against data privacy leakage-related reconstruction attacks.

本研究针对资源受限环境中量子计算能力缺乏的客户端及分割学习中的数据隐私泄露问题，提出了一种新的混合量子分割学习方法（HQSL）。该方法通过引入新的数据加载技术和噪声防御机制，实现了经典客户端与混合量子服务器的协同训练，同时提升了分类性能和安全性。实验结果表明，HQSL在多个数据集上都显著提高了模型的准确性和F1分数，并确认了其在客户端扩展中的可行性。

混合量子神经网络在分割学习中的应用