Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting

This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting. Exogenous predictors are incorporated by convexly fusing feature-specific kernels. For both quantum and classical models, the only tuned quantities are the feature-mixing weights and the KRR ridge α; classical hyperparameters (γ, 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. Introduction Quantum Machine Learning (QML) is an emerging discipline that combines the principles of quantum physics with traditional machine learning (ML) to exploit the distinctive characteristics of quantum systems, including superposition and entanglement phenomena [1]. This distinction facilitates the expeditious execution of certain tasks [2], such as classification and dimensionality reduction, where QML has demonstrated significant acceleration [3]. QML applications have extended to time-series data, leveraging quantum phenomena to model complex temporal dependencies. The goal is to enhance the results of traditional tasks by performing computations on qubits, which can process data more efficiently than classical bits [4, 5]. For example, Thakkar et al. [6] demonstrated that quantum machine-learning methods could enhance financial forecasting by improving both churn prediction and credit-risk assessment. Likewise, Kea et al. [7] developed a hybrid quantum-classical Long Short-Term Memory (QLSTM) to improve stock-price forecasting by leveraging quantum data encoding and high-dimensional quantum representations.