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Reservoir Computing via Quantum Recurrent Neural Networks

arXiv 2211.02612 ↗First posted 4 Nov 2022cs.NE cs.LG quant-phcited by 14

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Abstract (arXiv)

Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based methods require significant computational resources to perform the gradient-based optimization of a larger number of quantum circuit parameters. The major drawback is that such quantum gradient calculation requires a large amount of circuit evaluation, posing challenges in current near-term quantum hardware and simulation software. In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU. The main idea to this RC approach is that the QRNN with randomly initialized weights is treated as a dynamical system and only the final classical linear layer is trained. Our numerical simulations show that the QRNN-RC can reach results comparable to fully trained QRNN models for several function approximation and time series prediction tasks. Since the QRNN training complexity is significantly reduced, the proposed model trains notably faster. In this work we also compare to corresponding classical RNN-based RC implementations and show that the quantum version learns faster by requiring fewer training epochs in most cases. Our results demonstrate a new possibility to utilize quantum neural network for sequential modeling with greater quantum hardware efficiency, an important design consideration for noisy intermediate-scale quantum (NISQ) computers.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 50 of its 119 references that are in the map. First posted on arXiv 4 Nov 2022.

Builds on

50 of 119 references.

References · 50

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Cited by year (2026 to 7 Oct)

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

14 papers.

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  1. 2024Learning to Program Variational Quantum Circuits with Fast Weightscited by 23
  2. 2023Asynchronous training of quantum reinforcement learningcited by 20
  3. 2023Efficient quantum recurrent reinforcement learning via quantum reservoir computingcited by 14
  4. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learningcited by 13
  5. 2024An Introduction to Quantum Reinforcement Learning (QRL)cited by 7
  6. 2025Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generationcited by 7
  7. 2023Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gatingcited by 5
  8. 2025Quantum Long Short-term Memory with Differentiable Architecture Searchcited by 4
  9. 2026Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learningcited by 4
  10. 2023Quantum deep Q learning with distributed prioritized experience replaycited by 2
  11. 2026Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptationcited by 2
  12. 2026Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learningcited by 2
2 more
  1. 2025Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equationscited by 1
  2. 2025Quantum-Assisted Machine Learning Models for Enhanced Weather Predictioncited by 0