NAC-QFL: Noise Aware Clustered Quantum Federated Learning

TL;DR

This paper proposes a noise-aware clustered quantum federated learning system that mitigates noise, reduces communication costs, and improves performance in distributed quantum machine learning through device clustering and circuit partitioning.

Contribution

It introduces a novel noise-aware clustering and circuit partitioning approach to enhance distributed QML performance and address noise and communication challenges.

Findings

Improved QML accuracy on noisy datasets

Reduced quantum communication costs

Enhanced device noise mitigation

Abstract

Recent advancements in quantum computing, alongside successful deployments of quantum communication, hold promises for revolutionizing mobile networks. While Quantum Machine Learning (QML) presents opportunities, it contends with challenges like noise in quantum devices and scalability. Furthermore, the high cost of quantum communication constrains the practical application of QML in real-world scenarios. This paper introduces a noise-aware clustered quantum federated learning system that addresses noise mitigation, limited quantum device capacity, and high quantum communication costs in distributed QML. It employs noise modelling and clustering to select devices with minimal noise and distribute QML tasks efficiently. Using circuit partitioning to deploy smaller models on low-noise devices and aggregating similar devices, the system enhances distributed QML performance and reduces communication costs. Leveraging circuit cutting, QML techniques are more effective for smaller circuit sizes and fidelity. We conduct experimental evaluations to assess the performance of the proposed system. Additionally, we introduce a noisy dataset for QML to demonstrate the impact of noise on proposed accuracy.