Quantum-Train Long Short-Term Memory: Application on Flood Prediction Problem

TL;DR

This paper introduces a quantum-trained LSTM model for flood prediction that significantly reduces training parameters using quantum machine learning, enhancing efficiency and practicality for real-world disaster forecasting.

Contribution

It presents a novel quantum training technique for LSTM models that reduces parameters and operates without quantum hardware, improving flood prediction efficiency.

Findings

Parameter reduction to polylogarithmic scale

Operates independently of quantum hardware post-training

Effective for real-world flood forecasting

Abstract

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.