Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting

--We present the Quantum Kernel-Based Long Short-T erm Memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate time-series forecasting tasks, such as Air Quality Index (AQI) prediction. Leveraging quantum kernel methods allows for efficient computation of inner products in quantum spaces, addressing the computational challenges faced by classical models and variational quantum circuit-based models. Designed for the Noisy Intermediate-Scale Quantum (NISQ) era, QK-LSTM supports scalable hybrid quantum-classical implementations. Experimental results demonstrate that QK-LSTM outperforms classical LSTM networks in AQI forecasting, showcasing its potential for environmental monitoring and resource-constrained scenarios, while highlighting the broader applicability of quantum-enhanced machine learning frameworks in tackling large-scale, high-dimensional climate datasets. Climate time-series forecasting is essential for understanding and predicting environmental phenomena, which has significant implications for public health [1], resource management [2], and policy-making [3]. Accurate forecasting of climatic variables such as temperature, precipitation, and pollutant concentrations enables proactive measures to mitigate adverse effects associated with climate variability and change.