{"ID":2876172,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00711","arxiv_id":"2509.00711","title":"Resting-state fMRI Analysis using Quantum Time-series Transformer","abstract":"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.","short_abstract":"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 wi...","url_abs":"https://arxiv.org/abs/2509.00711","url_pdf":"https://arxiv.org/pdf/2509.00711v1","authors":"[\"Junghoon Justin Park\",\"Jungwoo Seo\",\"Sangyoon Bae\",\"Samuel Yen-Chi Chen\",\"Huan-Hsin Tseng\",\"Jiook Cha\",\"Shinjae Yoo\"]","published":"2025-08-31T06:08:57Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CE\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}