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A Quantum Variational Approach to Prototypical Recurrent Unit

arXiv 2609.04354 ↗First posted 3 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

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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References · 15

  1. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  2. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  3. 2020Variational Quantum Algorithmscited by 692
  4. 2018Supervised learning with quantum enhanced feature spacescited by 665
  5. 2020Quantum Long Short-Term Memorycited by 120
  6. 2021The Inductive Bias of Quantum Kernelscited by 91
  7. 2018Continuous-variable quantum neural networkscited by 74
  8. 2020Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spacescited by 44
  9. 2023Quantum Recurrent Neural Networks for Sequential Learningcited by 25
  10. 2022Rapid training of quantum recurrent neural networkscited by 13
  11. 2024Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamicscited by 9
  12. 2022Scalable Quantum Neural Networks for Classificationcited by 6
3 more
  1. 2023Variational Quantum Neural Networks (VQNNS) in Image Classificationcited by 4
  2. 2023A Novel Stochastic LSTM Model Inspired by Quantum Machine Learningcited by 2
  3. 2016Prototypical Recurrent Unitcited by 1

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