A Quantum Variational Approach to Prototypical Recurrent Unit
Mahyar Sadeghi Garjan, Tommaso Cesari, Michel Barbeau
cs.LG
Abstract
We introduce a lightweight Quantum Prototypical Recurrent Unit (QPRU) that requires significantly fewer parameters than both classical recurrent architectures, such as Long Short- Term Memory (LSTM) and Gated Recurrent Unit (GRU), and quantum variants, including Quantum LSTM (QLSTM) and Quantum GRU (QGRU). Despite its compact design, the QPRU achieves competitive forecasting performance, matching state-of-the-art baselines while offering important structural and practical advantages, including enhanced scalability and a reduced number of trainable parameters.
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Classified with taxonomy v2 on Mon, 7 Sept 2026.