标题： 基于量子核的长短期记忆网络在气候时间序列预测中的应用 显示英文标题

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

摘要： 我们提出基于量子核的长短期记忆（QK-LSTM）网络，该网络将量子核方法集成到经典LSTM架构中，以提高气候时间序列预测任务（如空气质量指数（AQI）预测）的预测准确性和计算效率。 通过将经典输入嵌入高维量子特征空间，QK-LSTM能够以更少的可训练参数捕捉复杂的非线性依赖关系和时间动态。 利用量子核方法可以高效计算量子空间中的内积，解决了经典模型和变分量子电路模型所面临的计算挑战。 QK-LSTM专为噪声中等规模量子（NISQ）时代设计，支持可扩展的混合量子-经典实现。 实验结果表明，QK-LSTM在AQI预测中优于经典LSTM网络，展示了其在环境监测和资源受限场景中的潜力，同时突显了量子增强机器学习框架在处理大规模、高维气候数据集方面的广泛应用前景。 显示英文摘要

摘要： We present the Quantum Kernel-Based Long short-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. By embedding classical inputs into high-dimensional quantum feature spaces, QK-LSTM captures intricate nonlinear dependencies and temporal dynamics with fewer trainable parameters. 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.