用量子核方法增强LSTM，提升空气质量预测精度与效率
Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting
- 将经典输入映射到高维量子空间，捕捉复杂时序依赖
- 在空气质量指数预测中优于传统LSTM，参数更少、计算更快
- 适合资源受限环境，为气候大数据提供可扩展量子增强方案
我们提出量子核增强的长短期记忆网络（QK-LSTM），将量子核方法融入经典LSTM架构，以提升气候时间序列预测任务（如空气质量指数，AQI）的预测准确率和计算效率。通过将经典输入嵌入高维量子特征空间，QK-LSTM以更少的可训练参数捕捉复杂的非线性依赖关系与时间动态。利用量子核方法高效计算量子空间中的内积，克服了经典模型及变分量子电路模型面临的计算瓶颈。该模型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。实验表明，QK-LSTM在AQI预测中优于经典LSTM，展现出在环境监测与资源受限场景中的潜力，凸显量子增强机器学习框架在处理大规模、高维气候数据方面的广泛应用前景。
原文摘要 · Abstract (English)
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.
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