When Geomorphology Meets Quantum Computing: a Quantum Machine Learning Model for Extreme Flows Classification

Landslides, debris flows, hyperconcentrated flows, and floods are among the most dangerous natural hazards worldwide. One of the fundamental tasks for geomorphologists is to classify and identify the kinds of processes they observe in the field. The task is more challenging than it sounds, especially considering high-damage processes like debris flows and landslides. Meanwhile, multiple dimensionless numbers (e.g., Reynolds number and Einstein number) based on first-principle physics have been widely used to describe these natural flows. When we use these dimensionless numbers and datasets to classify the flow, we face a long-standing challenge in machine learning: the curse of dimensionality. One of the expertise for quantum machine learning methods (e.g., Quantum Support Vector Machine, QSVM) is to deal with such a high-dimensional dataset. Therefore, based on Quantum machine learning methods, we develop a framework to objectively define the type of natural flows using the dimensionless number. Our preliminary results show that the QSVM method has very similar outputs compared to classical SVM, but it is relatively slower than classical ones. Meanwhile, for the high-dimensional k-mean cluster, the Quantum K-mean  model has shown different clusters compared to the classical version. In the future, we will develop a hybrid version combining classical K-mean with Quantum acceleration to understand different flow types.