mlsanityData to 7 Oct 2026

Rapid training of quantum recurrent neural networks

arXiv 2207.00378 ↗First posted 1 Jul 2022quant-ph cs.LGcited by 13

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Abstract (arXiv)

Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness Recurrent Neural Networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus, time and energy consuming. Here, we propose to extend the concept of RRNs by including continuous-variable quantum resources in it, and to use a quantum-enhanced RNN to overcome these obstacles. The design of the Continuous-Variable Quantum RNN (CV-QRNN) is rooted in the continuous-variable quantum computing paradigm. By performing extensive numerical simulations, we demonstrate that the quantum network is capable of learning-time dependence of several types of temporal data, and that it converges to the optimal weights in fewer epochs than a classical network. Furthermore, for a small number of trainable parameters, it can achieve lower losses than its classical counterpart. CV-QRNN can be implemented using commercially available quantum-photonic hardware.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 26 of its 83 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

26 of 83 references.

References · 26

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14 more
  1. 2021Quantum advantage in learning from experimentscited by 186
  2. 2020Quantum Long Short-Term Memorycited by 118
  3. 2014Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognitioncited by 111
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 4; 2026: 5.

13 papers.

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  1. 2023Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Imagescited by 10
  2. 2025A Primer on Quantum Machine Learningcited by 9
  3. 2025Feedback-driven recurrent quantum neural network universalitycited by 5
  4. 2023Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Predictioncited by 4
  5. 2026Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptationcited by 2
  6. 2026Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learningcited by 2
  7. 2023A Survey of Classical And Quantum Sequence Modelscited by 1
  8. 2025Hybrid Quantum-Classical Recurrent Neural Networkscited by 1
  9. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  10. 2024Multi-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classificationcited by 0
  11. 2025FRQI Pairs method for image classification using Quantum Recurrent Neural Networkcited by 0
  12. 2026A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitationscited by 0
1 more
  1. 2026A Quantum Variational Approach to Prototypical Recurrent Unitcited by 0