mlsanityData to 14 Oct 2026

Variational Quantum Soft Actor-Critic

arXiv 2112.11921 ↗First posted 20 Dec 2021quant-ph cs.AI cs.LGcited by 15

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Abstract (arXiv)

Quantum computing has a superior advantage in tackling specific problems, such as integer factorization and Simon's problem. For more general tasks in machine learning, by applying variational quantum circuits, more and more quantum algorithms have been proposed recently, especially in supervised learning and unsupervised learning. However, little work has been done in reinforcement learning, arguably more important and challenging. Previous work in quantum reinforcement learning mainly focuses on discrete control tasks where the action space is discrete. In this work, we develop a quantum reinforcement learning algorithm based on soft actor-critic -- one of the state-of-the-art methods for continuous control. Specifically, we use a hybrid quantum-classical policy network consisting of a variational quantum circuit and a classical artificial neural network. Tested in a standard reinforcement learning benchmark, we show that this quantum version of soft actor-critic is comparable with the original soft actor-critic, using much less adjustable parameters. Furthermore, we analyze the effect of different hyper-parameters and policy network architectures, pointing out the importance of architecture design for quantum reinforcement learning.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 27 of its 63 references that are in the map. First posted on arXiv 20 Dec 2021.

Builds on

27 of 63 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2015Continuous control with deep reinforcement learningcited by 3,407
  5. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  6. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  7. 2016OpenAI Gymcited by 1,736
  8. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  9. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  10. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
  11. 2018Supervised learning with quantum enhanced feature spacescited by 665
  12. 2019Parameterized quantum circuits as machine learning modelscited by 332
15 more
  1. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 303
  2. 2021Reinforcement learning based recommender systems: A surveycited by 188
  3. 2018Understanding the impact of entropy on policy optimizationcited by 176
  4. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  5. 2019Efficient Learning for Deep Quantum Neural Networkscited by 161
  6. 2018Quantum generative adversarial networkscited by 128
  7. 2019Transfer learning in hybrid classical-quantum neural networkscited by 114
  8. 2018Deep Reinforcement Learning for Page-wise Recommendationscited by 100
  9. 2021Parametrized quantum policies for reinforcement learningcited by 90
  10. 2021DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learningcited by 66
  11. 2020Quantum reinforcement learning in continuous action spacecited by 29
  12. 2021Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementationcited by 23
  13. 2021Deep Reinforcement Learning with Quantum-inspired Experience Replaycited by 19
  14. 2019A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealerscited by 15
  15. 2019Deep Reinforcement Learning Control of Quantum Cartpolescited by 10

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Citing papers per year: 2022: 4; 2023: 4; 2024: 5; 2025: 2.

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  1. 2022A Survey on Quantum Reinforcement Learningcited by 53
  2. 2022Quantum deep recurrent reinforcement learningcited by 24
  3. 2024Learning to Program Variational Quantum Circuits with Fast Weightscited by 24
  4. 2023Asynchronous training of quantum reinforcement learningcited by 20
  5. 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 16
  6. 2023Efficient quantum recurrent reinforcement learning via quantum reservoir computingcited by 14
  7. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learningcited by 14
  8. 2024Quantum Advantage Actor-Critic for Reinforcement Learningcited by 9
  9. 2022Variational Quantum Soft Actor-Critic for Robotic Arm Controlcited by 8
  10. 2023Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Carscited by 8
  11. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learningcited by 8
  12. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory searchcited by 3
3 more
  1. 2023Quantum deep Q learning with distributed prioritized experience replaycited by 2
  2. 2025Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimizationcited by 1
  3. 2025Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learningcited by 0