Quantum Computing and Machine Learning for EEG Signal Denoising: A Review
Abstract
Electroencephalography (EEG) signal processing faces persistent challenges from various noise sources, limiting its clinical and research applications. This review critically examines the emerging intersection of quantum computing and machine learning techniques for denoising EEG signals through September 2025. We systematically analyze both classical and quantum approaches, including recent breakthrough implementations such as the ICASSP 2025 quantum multimodal contrastive learning framework, which achieves 20.3% top-5 accuracy and quantum-enhanced architectures like QEEGNet, demonstrating 42.1% versus 39.8% baseline accuracy. Our analysis of peer-reviewed studies reveals that while quantum approaches show promise in specific scenarios, current hardware limitations prevent a definitive quantum advantage. A comprehensive 2025 Nature systematic review of 4,915 quantum machine learning studies in digital health found that only 0.33% (16 studies) met quality criteria for realistic operating conditions, and there was no consistent evidence of quantum advantage in biomedical applications. Classical methods achieving 78-85% accuracy with 8-40ms latency remain superior for most practical applications. This paper provides a comprehensive assessment of current methodologies, realistic performance benchmarks, technical challenges, and future research directions, offering a balanced perspective on both the genuine advances and fundamental limitations of quantum-enhanced neurological…
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