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Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network
Zhi-Ping Liu1,2, Xiao-Yu Cao1,2, Hao-Wen Liu1,2
1National Laboratory of Solid State Microstructures and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
Research (Washington, D.C.)
|June 12, 2026
Summary
Quantum federated learning (QFL) enhances data privacy using quantum networks. This new protocol, QuNetQFL, offers information-theoretic security and improves model accuracy on quantum and real-world datasets.
Area of Science:
- Quantum computing
- Artificial intelligence
- Cybersecurity
Background:
- Federated learning (FL) enables decentralized training but faces privacy risks in the quantum era.
- Quantum federated learning (QFL) promises enhanced security and efficiency but lacks practical, experimentally validated protocols.
- A need exists for quantum-secure methods to protect data privacy during distributed machine learning.
Purpose of the Study:
- To present and experimentally validate QuNetQFL, a novel QFL protocol for quantum networks.
- To demonstrate information-theoretic security in QFL through distributed quantum secret keys.
- To assess the performance and scalability of QuNetQFL on diverse datasets and tasks.
Main Methods:
- Implementation of QuNetQFL on a 4-client quantum network.
- Masking local model updates with distributed quantum secret keys for secure aggregation.
- Experimental validation using quantum and real-world datasets for classification and language tasks.
- Large-scale simulations for scalability assessment in handwritten-digit recognition.
Main Results:
- Experimental validation on a 4-client quantum network confirmed protocol functionality.
- Improved global accuracy in classifying quantum datasets by adding a single quantum client.
- Comparable and robust performance for sentiment analysis using federated fine-tuning of a hybrid model.
- Scalability demonstrated to 200 clients for digit recognition with significant communication cost reduction.
Conclusions:
- QuNetQFL provides a practical and experimentally validated approach to quantum-secure federated learning.
- The protocol offers information-theoretic security, enhancing privacy in distributed quantum machine learning.
- QuNetQFL represents a scalable solution for the emerging quantum internet, applicable to various AI tasks.
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