[Submitted on 3 Sep 2026]
Title:A Quantum Variational Approach to Prototypical Recurrent Unit
View a PDF of the paper titled A Quantum Variational Approach to Prototypical Recurrent Unit, by Mahyar Sadeghi Garjan and 2 other authors
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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.
Comments: 12 pages, 5 figures, 4 tables
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2609.04354 [cs.LG]
(or arXiv:2609.04354v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.04354
arXiv-issued DOI via DataCite (pending registration)
Journal reference: 2026 IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC), pp. 776-780, 2026
Related DOI:
https://doi.org/10.1109/QCNC69040.2026.00126
DOI(s) linking to related resources
Submission history
From: Tommaso Cesari [view email] [v1] Thu, 3 Sep 2026 18:19:07 UTC (220 KB)
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