Long COVID is a multisystem condition characterized by persistent symptoms such as fatigue, cognitive impairment, and systemic inflammation, following COVID-19 infection, yet its mechanisms remain poorly understood. In this study, we applied quantum walk (QW), a computational approach leveraging quantum interference, to explore large-scale SARS-CoV-2-induced protein (SIP) networks. Compared to the conventional random walk with restart (RWR) method, QW demonstrated superior capacity to traverse deeper regions of the network, uncovering proteins and pathways implicated in Long COVID. Key findings include mitochondrial dysfunction, thromboinflammatory responses, and neuronal inflammation as central mechanisms. QW uniquely identified the CDGSH iron-sulfur domain-containing protein family and VDAC1, a mitochondrial calcium transporter, as critical regulators of these processes. VDAC1 emerged as a potential biomarker and therapeutic target, supported by FDA-approved compounds such as cannabidiol. These findings highlight QW as a powerful tool for elucidating complex biological systems and identifying novel therapeutic targets for conditions like Long COVID.
@article{arxiv.2501.15208,
title = {Advancing Understanding of Long COVID Pathophysiology Through Quantum Walk-Based Network Analysis},
author = {Jaesub Park and Woochang Hwang and Seokjun Lee and Hyun Chang Lee and Méabh MacMahon and Matthias Zilbauer and Namshik Han},
journal= {arXiv preprint arXiv:2501.15208},
year = {2025}
}