用量子傅里叶变换设计新核函数，提升太阳能辐照度预测精度。
Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting
- 基于量子傅里叶变换与保护旋转层构建量子核函数
- 在多站点数据上实现更高的R2、更低的nRMSE与偏置
- 适合关注量子机器学习在能源预测中应用的研究者
本研究提出一种基于量子傅里叶变换（QFT）的增强型量子核函数，用于短期时间序列预测。信号经窗口化与幅值编码后，通过QFT变换，并经保护性旋转层防止QFT与其伴随算子抵消；该核函数用于核岭回归（KRR）。外生变量通过特征专属核的凸融合引入。在覆盖柯本气候类型的多站太阳能辐照度数据上，所提核函数在中位数R2、nRMSE和nMBE方面均优于经典径向基函数（RBF）与多项式核，且在剧烈变化时段仍具误差余量。所有模型仅需调整特征混合权重与KRR正则化参数alpha，经典超参数（gamma, r, d）固定，验证集大小一致。实验在无噪声模拟器（5量子比特，窗口长度L=32）上进行。讨论了局限性与消融分析，并指明通往NISQ设备执行的路径。
原文摘要 · Abstract (English)
This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting. Each signal is windowed, amplitude-encoded, transformed by a QFT, then passed through a protective rotation layer to avoid the QFT/QFT adjoint cancellation; the resulting kernel is used in kernel ridge regression (KRR). Exogenous predictors are incorporated by convexly fusing feature-specific kernels. On multi-station solar irradiance data across Koppen climate classes, the proposed kernel consistently improves median R2 and nRMSE over reference classical RBF and polynomials kernels, while also reducing bias (nMBE); complementary MAE/ERMAX analyses indicate tighter average errors with remaining headroom under sharp transients. For both quantum and classical models, the only tuned quantities are the feature-mixing weights and the KRR ridge alpha; classical hyperparameters (gamma, r, d) are fixed, with the same validation set size for all models. Experiments are conducted on a noiseless simulator (5 qubits; window length L=32). Limitations and ablations are discussed, and paths toward NISQ execution are outlined.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。