# Quantum Split Neural Network Learning using Cross-Channel Pooling

arXiv: https://arxiv.org/abs/2211.06524 · HTML: https://mlsanity.com/paper/2211.06524/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Information-theoretic bounds on quantum advantage in machine learning](https://mlsanity.com/paper/2101.02464/) (2021)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Quantum Adversarial Machine Learning](https://mlsanity.com/paper/2001.00030/) (2019)
- [Quantum Entanglement in Deep Learning Architectures](https://mlsanity.com/paper/1803.09780/) (2018)
- [Quantum federated learning through blind quantum computing](https://mlsanity.com/paper/2103.08403/) (2021)
- [Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design](https://mlsanity.com/paper/2203.10443/) (2022)
- [QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning](https://mlsanity.com/paper/2202.13239/) (2022)
- [Slimmable Quantum Federated Learning](https://mlsanity.com/paper/2207.10221/) (2022)
- [Quantum Multi-Agent Meta Reinforcement Learning](https://mlsanity.com/paper/2208.11510/) (2022)
- [An Introduction to Quantum Machine Learning for Engineers](https://mlsanity.com/paper/2205.09510/) (2022)
- [Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications](https://mlsanity.com/paper/2202.11200/) (2022)
- [Spatio-Temporal Split Learning](https://mlsanity.com/paper/2108.06309/) (2021)
- [Scalable Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2209.12372/) (2022)
- [Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet](https://mlsanity.com/paper/2210.04308/) (2022)
- [Dual-Frequency Quantum Phase Estimation Mitigates the Spectral Leakage of Quantum Algorithms](https://mlsanity.com/paper/2201.09323/) (2022)
- [3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications](https://mlsanity.com/paper/2210.09728/) (2022)
- [Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification](https://mlsanity.com/paper/2210.16731/) (2022)

## Built on by (most cited first, 4 of 4)

- [Towards Quantum Federated Learning](https://mlsanity.com/paper/2306.09912/) (2023)
- [eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels](https://mlsanity.com/paper/2405.17486/) (2024)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [Quantum Federated Learning: Architectural Elements and Future Directions](https://mlsanity.com/paper/2510.17642/) (2025)

## Abstract (arXiv)

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.