QTS2D: Quantum-based image encoding of time series

Despite growing interest in quantum machine learning, the application of quantum principles to foundational tasks such as time series feature engineering remains underexplored. This paper introduces QTS2D, a Python library that addresses this gap by adapting established time series-to-image techniques, including Gramian Angular Fields, Markov Transition Fields, Recurrence Plots, and Spectrograms, using quantum-inspired formulations. Unlike many works focused on execution speed or hardware advantage, our primary aim is to investigate the representational power of quantum-derived transformations for feature extraction. QTS2D leverages concepts such as amplitude encoding, quantum fidelity, and the Quantum Fourier Transform to generate rich, structured representations simulated on classical hardware. Empirical results on physiological signals demonstrate that quantum-based representations not only improve classification accuracy over classical counterparts, even within fully classical machine learning pipelines, but also exhibit enhanced class separability in UMAP embeddings and consistently lower Davies–Bouldin Index scores. The library provides a practical and extensible foundation for exploring quantum-enhanced feature engineering, with promising applications in health monitoring, anomaly detection, and beyond.