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A Quantum Variational Approach to Prototypical Recurrent Unit

arXiv机器学习 2026-09-07 12:00 4 阅读 查看原文
arXiv:2609.04354 (cs)

Title:A Quantum Variational Approach to Prototypical Recurrent Unit

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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.
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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
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

Submission history

From: Tommaso Cesari [view email]
[v1] Thu, 3 Sep 2026 18:19:07 UTC (220 KB)
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