qinnovision-world-challenge-2025 / Predicting Flash Floods

Climate change has led to unprecedented torrential rainfall in our region, causing devastating floods. This challenge presents an opportunity to apply quantum machine learning (QML) to enhance flash flood prediction. We seek innovative QML solutions that address the complexities of imbalanced datasets and demonstrate practical implementation. The goal is to improve prediction accuracy and efficiency, ultimately contributing to disaster mitigation efforts.

This challenge aims to develop a robust and accurate flash flood prediction model using quantum machine learning techniques. The model should provide early warnings for severe flash floods to enable effective disaster management, which depends on the quality of prediction and estimation of disaster losses.

Participants are invited to develop and optimise their quantum circuits using our QISKIT transpile function, as illustrated in the second step of QISKIT Patterns- https://docs.quantum.ibm.com/guides/intro-to-patterns. Subsequently, we highly encourage participants to run their circuits on a fake backend using the QISKIT Runtime local testing mode- https://docs.quantum.ibm.com/guides/local-testing-mode to achieve comparable results to the ones we demonstrated in the references below to ensure consistency.