# Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications

arXiv: https://arxiv.org/abs/2202.11200 · HTML: https://mlsanity.com/paper/2202.11200/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition](https://mlsanity.com/paper/2010.13309/) (2020)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [Quantum federated learning through blind quantum computing](https://mlsanity.com/paper/2103.08403/) (2021)
- [Quantum Deep Learning](https://mlsanity.com/paper/1412.3489/) (2014)
- [QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks](https://mlsanity.com/paper/2110.03861/) (2021)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation](https://mlsanity.com/paper/2108.06849/) (2021)
- [Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks](https://mlsanity.com/paper/2112.02543/) (2021)
- [Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data](https://mlsanity.com/paper/2108.10147/) (2021)
- [Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms](https://mlsanity.com/paper/2001.02932/) (2020)
- [Variational Quantum Algorithms for Dimensionality Reduction and Classification](https://mlsanity.com/paper/1910.12164/) (2019)

## Built on by (most cited first, 13 of 13)

- [Towards Quantum Federated Learning](https://mlsanity.com/paper/2306.09912/) (2023)
- [Scalable Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2209.12372/) (2022)
- [Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation](https://mlsanity.com/paper/2306.11297/) (2023)
- [Quantum Split Neural Network Learning using Cross-Channel Pooling](https://mlsanity.com/paper/2211.06524/) (2022)
- [Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding](https://mlsanity.com/paper/2212.01732/) (2022)
- [Machine Learning Applications of Quantum Computing: A Review](https://mlsanity.com/paper/2406.13262/) (2024)
- [Distributed and Secure Kernel-Based Quantum Machine Learning](https://mlsanity.com/paper/2408.10265/) (2024)
- [Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning](https://mlsanity.com/paper/2211.15375/) (2022)
- [Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes](https://mlsanity.com/paper/2307.09711/) (2023)
- [Classi|Qrangle Towards a Translation Framework To Bridge The Classical-Quantum Programming Gap](https://mlsanity.com/paper/2406.06764/) (2024)
- [QFed: Parameter-Compact Quantum-Classical Federated Learning](https://mlsanity.com/paper/2601.09809/) (2026)
- [FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy](https://mlsanity.com/paper/2605.08324/) (2026)
- [A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading](https://mlsanity.com/paper/2610.17836/) (2026)

## Abstract (arXiv)

Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem, quantum deep learning (QDL) and distributed deep learning (DDL) has emerged to complement existing DL methods. Furthermore, a quantum distributed deep learning (QDDL) technique that combines and maximizes these advantages is getting attention. This paper compares several model structures for QDDL and discusses their possibilities and limitations to leverage QDDL for some representative application scenarios.