QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology
Nanting Huang, Xiaoyu Li, Xin Yang, Li Xie, Guowu Yang, Liujiang Zhou · 2026
Read QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology by Nanting Huang, Xiaoyu Li, Xin Yang, Li Xie, Guowu Yang, Liujiang Zhou (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13363761.txt.
Description
Nanting Huang and colleagues present a 2026 study on a hybrid classical-quantum network for spotting autism spectrum disorder in brain scans. Autism seems to mix local activity quirks with whole-brain wiring faults, so their design reads both at once. A wide residual network reads local activity maps. A small quantum circuit reads the connectivity matrix, using quantum state space to catch long-range patterns with few settings.
Trained and tested on resting brain scans, the fused model reached about 68 percent accuracy, ahead of ten rival methods they compared. Its readouts also flagged core disease hubs on a standard brain atlas. The authors present the tool as decision support for scan-based diagnosis. They cast the quantum branch as a guard against overfitting on small medical datasets.
Catalog record
| CREATOR | Nanting Huang, Xiaoyu Li, Xin Yang, Li Xie, Guowu Yang, Liujiang Zhou |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71265 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |