Resting-state fMRI Analysis using Quantum Time-series Transformer
This work addresses computational bottlenecks in neuroscience for researchers and clinicians, offering a more efficient method for analyzing brain connectivity and identifying biomarkers, though it appears incremental as it builds on existing transformer and quantum computing concepts.
The paper tackled the computational inefficiency of classical transformers in resting-state fMRI analysis by introducing a Quantum Time-series Transformer, which achieved comparable or superior predictive performance on large-scale datasets like the Adolescent Brain Cognitive Development Study and UK Biobank, with notable gains in small-sample scenarios.
Resting-state functional magnetic resonance imaging (fMRI) has emerged as a pivotal tool for revealing intrinsic brain network connectivity and identifying neural biomarkers of neuropsychiatric conditions. However, classical self-attention transformer models--despite their formidable representational power--struggle with quadratic complexity, large parameter counts, and substantial data requirements. To address these barriers, we introduce a Quantum Time-series Transformer, a novel quantum-enhanced transformer architecture leveraging Linear Combination of Unitaries and Quantum Singular Value Transformation. Unlike classical transformers, Quantum Time-series Transformer operates with polylogarithmic computational complexity, markedly reducing training overhead and enabling robust performance even with fewer parameters and limited sample sizes. Empirical evaluation on the largest-scale fMRI datasets from the Adolescent Brain Cognitive Development Study and the UK Biobank demonstrates that Quantum Time-series Transformer achieves comparable or superior predictive performance compared to state-of-the-art classical transformer models, with especially pronounced gains in small-sample scenarios. Interpretability analyses using SHapley Additive exPlanations further reveal that Quantum Time-series Transformer reliably identifies clinically meaningful neural biomarkers of attention-deficit/hyperactivity disorder (ADHD). These findings underscore the promise of quantum-enhanced transformers in advancing computational neuroscience by more efficiently modeling complex spatio-temporal dynamics and improving clinical interpretability.