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This research proposes a lightweight Quantum Prototypical Recurrent Unit (QPRU) designed to outperform classical LSTMs and GRUs while using fewer parameters than existing quantum variants. The approach demonstrates competitive forecasting performance with enhanced scalability, addressing key efficiency challenges in hybrid AI-quantum architectures.
A Quantum Variational Approach to Prototypical Recurrent Unit
arXiv:2609.04354v1 Announce Type: new 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.
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.
Machine Learning (cs.LG)
arXiv:2609.04354 [cs.LG]
(or arXiv:2609.04354v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.04354 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
2026 IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC), pp. 776-780, 2026
https://doi.org/10.1109/QCNC69040.2026.00126 Focus to learn more DOI(s) linking to related resources
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