Khan et al. (2026) Quantum generative intelligence for rare hydroclimatic anomaly screening of glacial lake outburst flood risk

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-09-12
• Authors: Shah Nawaz Khan, Hiroshi Yamauchi, Rajib Shaw
• DOI: 10.1038/s41598-026-70477-7

Research Groups

• Department of Earth and Environmental Sciences, University of Tokyo
• Institute of Advanced Study, Kyoto University
• Disaster Risk Reduction Center, University of Tokyo

Short Summary

This study proposes a hybrid quantum-classical framework for rare hydroclimatic anomaly screening that integrates conditional multivariate sequence generation, quantum expectation-based latent representations, generated-reference temporal mismatch scoring, and supervised rare-event classification. The framework is applied to glacial lake outburst flood (GLOF) risk reduction in the Hindu Kush-Himalaya region.

Objective

• Investigate the potential of quantum machine learning for rare event prediction in hydroclimatic systems.
• Develop a hybrid quantum-classical framework for anomaly screening and rare-event classification.
• Apply the framework to GLOF risk reduction in the Hindu Kush-Himalaya region.

Study Configuration

• Spatial Scale: Regional (Hindu Kush-Himalaya)
• Temporal Scale: Monthly to annual

Methodology and Data

• Models used: Conditional Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), temporal convolutional sequence learning, and latent representations derived from parameterized quantum-circuit expectation values.
• Data sources: Satellite, observation, reanalysis datasets for hydroclimatic variables and glacier-static descriptors.

Main Results

• The proposed framework achieved lower errors in cross-feature correlation, Wasserstein distance, Kolmogorov-Smirnov statistic, autocorrelation distance, and power spectral density distance compared to Gaussian and sign latent baselines.
• The quantum-latent generated-reference mismatch scores supported rare-event screening under severe data imbalance.

Contributions

• This study contributes to disaster-risk governance by providing a high-recall screening approach for identifying hydroclimatic conditions associated with elevated GLOF susceptibility in data-scarce mountain regions.
• The proposed framework extends quantum generative intelligence into the domain of rare-event hydroclimatic representation learning under severe cryosphere data imbalance and sparse-event conditions.

Funding

• This research was funded by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 20H00645.
• The study also received support from the University of Tokyo's Disaster Risk Reduction Center.

Citation

@article{Khan2026Quantum,
  author = {Khan, Shah Nawaz and Yamauchi, Hiroshi and Shaw, Rajib},
  title = {Quantum generative intelligence for rare hydroclimatic anomaly screening of glacial lake outburst flood risk},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-70477-7},
  url = {https://doi.org/10.1038/s41598-026-70477-7}
}