mlsanityData to 7 Oct 2026

Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation

arXiv 2108.06849 ↗First posted 16 Aug 2021cs.LGcited by 23

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Abstract (arXiv)

The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies. Utilizing quantum circuit and quantum differential programming, many research are conducted such as textitQuantum Machine Learning (QML). In particular, quantum reinforcement learning is a good field to test the possibility of quantum machine learning, and a lot of research is being done. This work will introduce the concept of quantum reinforcement learning using a variational quantum circuit, and confirm its possibility through implementation and experimentation. We will first present the background knowledge and working principle of quantum reinforcement learning, and then guide the implementation method using the PennyLane library. We will also discuss the power and possibility of quantum reinforcement learning from the experimental results obtained through this work.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 16 Aug 2021.

Builds on

8 of 16 references.

References · 8

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2016OpenAI Gymcited by 1,684
  5. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  6. 2014Quantum Deep Learningcited by 31
  7. 2021Quantum Neural Networks: Concepts, Applications, and Challengescited by 21
  8. 2021Quantum Scheduling for Millimeter-Wave Observation Satellite Constellationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 3; 2024: 4; 2025: 2; 2026: 4.

23 papers.

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  1. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  2. 2022A Survey on Quantum Reinforcement Learningcited by 50
  3. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
  4. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  5. 2023Quantum Deep Hedgingcited by 22
  6. 2022Quantum Policy Gradient Algorithm with Optimized Action Decodingcited by 21
  7. 2021Variational Quantum Soft Actor-Criticcited by 14
  8. 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 14
  9. 2022Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applicationscited by 12
  10. 2024Quantum Advantage Actor-Critic for Reinforcement Learningcited by 9
  11. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learningcited by 7
  12. 2022Compositional optimization of quantum circuits for quantum kernels of support vector machinescited by 6
11 more
  1. 2023Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Carscited by 6
  2. 2023Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimizationcited by 5
  3. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory searchcited by 3
  4. 2024Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Roboticscited by 2
  5. 2026Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuitscited by 2
  6. 2025Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimizationcited by 1
  7. 2025Quantum feature-map learning with reduced resource overheadcited by 1
  8. 2026Learning to Coordinate via Quantum Entanglement in Multi-Agent Reinforcement Learningcited by 1
  9. 2022Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networkscited by 0
  10. 2026Securing SIM-Assisted Wireless Networks via Quantum Reinforcement Learningcited by 0
  11. 2026Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equationscited by 0