  Flood prediction is a critical challenge in the context of climate change,
with significant implications for ecosystem preservation, human safety, and
infrastructure protection. In this study, we tackle this problem by applying
the Quantum-Train (QT) technique to a forecasting Long Short-Term Memory (LSTM)
model trained by Quantum Machine Learning (QML) with significant parameter
reduction. The QT technique, originally successful in the A Matter of Taste
challenge at QHack 2024, leverages QML to reduce the number of trainable
parameters to a polylogarithmic function of the number of parameters in a
classical neural network (NN). This innovative framework maps classical NN
weights to a Hilbert space, altering quantum state probability distributions to
adjust NN parameters. Our approach directly processes classical data without
the need for quantum embedding and operates independently of quantum computing
resources post-training, making it highly practical and accessible for
real-world flood prediction applications. This model aims to improve the
efficiency of flood forecasts, ultimately contributing to better disaster
preparedness and response.
