mlsanityData to 7 Oct 2026

Quantum Reinforcement Learning via Policy Iteration

arXiv 2203.01889 ↗First posted 3 Mar 2022quant-ph cs.LGcited by 19

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Abstract (arXiv)

Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning. Reinforcement learning, on the other hand, has become essential for solving many decision making problems and policy iteration methods remain the foundation of such approaches. In this paper, we provide a general framework for performing quantum reinforcement learning via policy iteration. We validate our framework by designing and analyzing: quantum policy evaluation methods for infinite horizon discounted problems by building quantum states that approximately encode the value function of a policy π; and quantum policy improvement methods by post-processing measurement outcomes on these quantum states. Last, we study the theoretical and experimental performance of our quantum algorithms on two environments from OpenAI's Gym.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 3 Mar 2022.

Builds on

8 of 35 references.

References · 8

  1. 2013Intriguing properties of neural networkscited by 5,114
  2. 2016OpenAI Gymcited by 1,684
  3. 2016Quantum Machine Learningcited by 325
  4. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  5. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  6. 2016Quantum Recommendation Systemscited by 69
  7. 2018The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulationcited by 30
  8. 2021Quantum Algorithms for Reinforcement Learning with a Generative Modelcited by 24

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Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 5; 2025: 7.

19 papers.

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  1. 2022A Survey on Quantum Reinforcement Learningcited by 50
  2. 2023Quantum Deep Hedgingcited by 22
  3. 2022Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learningcited by 21
  4. 2023Asynchronous training of quantum reinforcement learningcited by 20
  5. 2022Quantum policy gradient algorithmscited by 15
  6. 2023Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regretcited by 9
  7. 2025Benchmarking Quantum Reinforcement Learningcited by 9
  8. 2024Warm-Start Variational Quantum Policy Iterationcited by 4
  9. 2024Fourier Analysis of Variational Quantum Circuits for Supervised Learningcited by 4
  10. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory searchcited by 3
  11. 2023Quantum deep Q learning with distributed prioritized experience replaycited by 2
  12. 2024Comprehensive Library of Variational LSE Solverscited by 2
7 more
  1. 2025GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuitscited by 1
  2. 2025Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environmentscited by 1
  3. 2025A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Modelcited by 1
  4. 2025Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimizationcited by 1
  5. 2024Differentiable Quantum Computing for Large-scale Linear Controlcited by 0
  6. 2025CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cuttingcited by 0
  7. 2025From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardwarecited by 0