Quantum Reupload Units: A Scalable and Expressive Approach for Time Series Learning
2025 IEEE International Conference on Quantum Computing and Engineering (QCE25 / IEEE Quantum Week 2025)
A single-qubit QRU architecture for time-series learning, evaluated on chaotic and real environmental data with matched classical and quantum baselines and an analysis of spectral expressivity.
Published by IEEE in the proceedings of QCE25, the paper introduces the Quantum Reupload Unit as a compact architecture for temporal learning. It connects repeated data encoding to functional expressivity and evaluates the model on synthetic and real time-series tasks under limited quantum resources.