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arXiv Machine Learning By Mahyar Sadeghi Garjan, Tommaso Cesari, Michel Barbeau

A Quantum Variational Approach to Prototypical Recurrent Unit

Read the original on arXiv Machine Learning →

The paper presents a lightweight Quantum Prototypical Recurrent Unit (QPRU) that uses far fewer parameters than classical recurrent models like LSTM and GRU, as well as quantum variants such as QLSTM and QGRU. Despite its compactness, the QPRU matches state‑of‑the‑art forecasting performance. It offers structural and practical benefits, notably improved scalability and a reduced parameter count.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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