mlsanityData to 7 Oct 2026

Quantum deep recurrent reinforcement learning

arXiv 2210.14876 ↗First posted 26 Oct 2022quant-ph cs.AI cs.ETcited by 23

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Abstract (arXiv)

Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems. Classical RL has been shown to be capable to solve various challenging tasks. However, RL algorithms in the quantum world are still in their infancy. One of the challenges yet to solve is how to train quantum RL in the partially observable environments. In this paper, we approach this challenge through building QRL agents with quantum recurrent neural networks (QRNN). Specifically, we choose the quantum long short-term memory (QLSTM) to be the core of the QRL agent and train the whole model with deep Q-learning. We demonstrate the results via numerical simulations that the QLSTM-DRQN can solve standard benchmark such as Cart-Pole with more stable and higher average scores than classical DRQN with similar architecture and number of model parameters.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 26 Oct 2022.

Builds on

21 of 41 references.

References · 21

  1. 2016OpenAI Gymcited by 1,684
  2. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 457
  3. 2020The power of quantum neural networkscited by 377
  4. 2021Generalization in quantum machine learning from few training datacited by 214
  5. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  6. 2020Quantum Long Short-Term Memorycited by 118
  7. 2021Federated Quantum Machine Learningcited by 82
  8. 2020Recurrent Quantum Neural Networkscited by 79
  9. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  10. 2021The Dawn of Quantum Natural Language Processingcited by 60
  11. 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 56
  12. 2021Quantum Federated Learning with Quantum Datacited by 53
9 more
  1. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  2. 2021An end-to-end trainable hybrid classical-quantum classifiercited by 49
  3. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  4. 2020Quantum reinforcement learning in continuous action spacecited by 27
  5. 2021QTN-VQC: An End-to-End Learning framework for Quantum Neural Networkscited by 27
  6. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
  7. 2018The Expressive Power of Parameterized Quantum Circuitscited by 18
  8. 2021Variational Quantum Soft Actor-Criticcited by 14
  9. 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 6; 2025: 7; 2026: 3.

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  1. 2023Asynchronous training of quantum reinforcement learningcited by 20
  2. 2025Learning to Measure Quantum Neural Networkscited by 15
  3. 2022Reservoir Computing via Quantum Recurrent Neural Networkscited by 14
  4. 2023Efficient quantum recurrent reinforcement learning via quantum reservoir computingcited by 14
  5. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learningcited by 13
  6. 2024Quantum Machine Learning Architecture Search via Deep Reinforcement Learningcited by 12
  7. 2024Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacitycited by 9
  8. 2023Quantum Federated Learning With Quantum Networkscited by 8
  9. 2024An Introduction to Quantum Reinforcement Learning (QRL)cited by 7
  10. 2025Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generationcited by 7
  11. 2025Learning to Program Quantum Measurements for Machine Learningcited by 7
  12. 2023Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimizationcited by 5
11 more
  1. 2023Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Predictioncited by 4
  2. 2025Quantum Long Short-term Memory with Differentiable Architecture Searchcited by 4
  3. 2026Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learningcited by 4
  4. 2025Transfer Learning Analysis of Variational Quantum Circuitscited by 3
  5. 2023Quantum deep Q learning with distributed prioritized experience replaycited by 2
  6. 2024Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learningcited by 2
  7. 2025Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architecturescited by 2
  8. 2026Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptationcited by 2
  9. 2026Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learningcited by 2
  10. 2025Neural Architecture Search for Quantum Autoencoderscited by 1
  11. 2024Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimizationcited by 0