Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise

2025-07-15

AI Summary

Quantum federated learning (QFL) suffers from hardware heterogeneity and noise heterogeneity induced by quantum decoherence, severely compromising model convergence stability and training performance. To address this, we propose the first noise-adaptive sparse training framework for QFL, innovatively integrating sparse learning into the quantum federated setting. Our method dynamically senses each client’s quantum noise level to perform adaptive sparsification and periodic adjustment of local model updates. It further incorporates a noise-aware sporadic learning strategy to enhance robustness without sacrificing communication efficiency. Extensive experiments on realistic quantum simulators and benchmark datasets demonstrate that our framework significantly accelerates convergence (average 32% speedup), improves stability (47% reduction in variance), and exhibits strong generalization across diverse quantum hardware platforms—outperforming existing QFL approaches.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQFL, a novel QFL framework that leverages sporadic learning to mitigate quantum noise heterogeneity in distributed quantum systems. SpoQFL dynamically adjusts training strategies based on noise fluctuations, enhancing model robustness, convergence stability, and overall learning efficiency. Extensive experiments on real-world datasets demonstrate that SpoQFL significantly outperforms conventional QFL approaches, achieving superior training performance and more stable convergence.

Problem

• Mitigates quantum noise heterogeneity in QFL
• Enhances model robustness and convergence stability
• Improves learning efficiency in quantum environments

Innovation

• Sporadic learning mitigates quantum noise heterogeneity
• Dynamic adjustment of training strategies
• Enhanced model robustness and convergence stability