mlsanityData to 7 Oct 2026

BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading

arXiv 2305.00905 ↗First posted 27 Apr 2023quant-ph cs.LGcited by 3

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Abstract (arXiv)

Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerbated in the case of batch RL, where the agent is trained solely on a pre-collected dataset without environment interactions. Recent advancements in quantum computing suggest that quantum models might require less data for training compared to classical methods. In this paper, we investigate this potential advantage by proposing a batch RL algorithm that utilizes VQC as function approximators within the discrete batch-constraint deep Q-learning (BCQ) algorithm. Additionally, we introduce a novel data re-uploading scheme by cyclically shifting the order of input variables in the data encoding layers. We evaluate the efficiency of our algorithm on the OpenAI CartPole environment and compare its performance to the classical neural network-based discrete BCQ.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 47 references that are in the map. First posted on arXiv 27 Apr 2023.

Builds on

19 of 47 references.

References · 19

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2016OpenAI Gymcited by 1,684
  4. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  5. 2019On the Convergence of Adam and Beyondcited by 834
  6. 2020The power of quantum neural networkscited by 377
  7. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 328
  8. 2021Generalization in quantum machine learning from few training datacited by 214
  9. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  10. 2019Benchmarking Batch Deep Reinforcement Learning Algorithmscited by 108
  11. 2022A Survey on Quantum Reinforcement Learningcited by 50
  12. 2019An Optimistic Perspective on Offline Reinforcement Learningcited by 37
7 more
  1. 2019Input Redundancy for Parameterized Quantum Circuitscited by 33
  2. 2020Hybrid quantum-classical classifier based on tensor network and variational quantum circuitcited by 26
  3. 2022Quantum Policy Gradient Algorithm with Optimized Action Decodingcited by 21
  4. 2020A scale-dependent notion of effective dimensioncited by 17
  5. 2022Uncovering Instabilities in Variational-Quantum Deep Q-Networkscited by 14
  6. 2022Incremental Data-Uploading for Full-Quantum Classificationcited by 13
  7. 2023Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Managementcited by 8

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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Model-based Offline Quantum Reinforcement Learningcited by 4
  2. 2024Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rulecited by 4
  3. 2024Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularizationcited by 4

Similar, not cited

Close in wording, no citation either way

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  3. 2025Quantile Q-Learning: Revisiting Offline Extreme Q-Learning with Quantile Regression
  4. 2023Quantum Imitation Learning
  5. 2020Deep Inverse Q-learning with Constraints
  6. 2023Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars
  7. 2026Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
  8. 2026DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search