# A Hybrid Quantum Neural Network for Split Learning

arXiv: https://arxiv.org/abs/2409.16593 · HTML: https://mlsanity.com/paper/2409.16593/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Reconstructing Training Data with Informed Adversaries](https://mlsanity.com/paper/2201.04845/) (2022)
- [Unleashing the Tiger: Inference Attacks on Split Learning](https://mlsanity.com/paper/2012.02670/) (2020)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [NoPeek: Information leakage reduction to share activations in distributed deep learning](https://mlsanity.com/paper/2008.09161/) (2020)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders](https://mlsanity.com/paper/1703.07980/) (2017)
- [Practical Defences Against Model Inversion Attacks for Split Neural Networks](https://mlsanity.com/paper/2104.05743/) (2021)
- [Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation](https://mlsanity.com/paper/2212.11614/) (2022)
- [Feature Space Hijacking Attacks against Differentially Private Split Learning](https://mlsanity.com/paper/2201.04018/) (2022)
- [Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)](https://mlsanity.com/paper/2109.02862/) (2021)
- [A hybrid quantum-classical neural network with deep residual learning](https://mlsanity.com/paper/2012.07772/) (2020)
- [A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud](https://mlsanity.com/paper/2310.00246/) (2023)
- [A Block-Ring connected Topology of Parameterized Quantum Circuits](https://mlsanity.com/paper/2308.10791/) (2023)

## Built on by (most cited first, 5 of 5)

- [When Federated Learning Meets Quantum Computing: Survey and Research Opportunities](https://mlsanity.com/paper/2504.08814/) (2025)
- [A Survey on Foundation Models for Personalized Federated Intelligence](https://mlsanity.com/paper/2505.06907/) (2025)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning](https://mlsanity.com/paper/2506.19275/) (2025)
- [Parameter-efficient Quantum Multi-task Learning](https://mlsanity.com/paper/2604.13560/) (2026)

## Abstract (arXiv)

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. Additionally, 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. Evaluations on real hardware demonstrate HQSL's practicality under realistic quantum noise. 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 train collaboratively with a hybrid quantum server, improving model performance and resistance against reconstruction attacks.