mlsanityData to 7 Oct 2026

Quantum Policy Gradient Algorithm with Optimized Action Decoding

arXiv 2212.06663 ↗First posted 13 Dec 2022quant-ph cs.LGcited by 21

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Abstract (arXiv)

Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on applications in quantum reinforcement learning, we propose a specific action decoding procedure for a quantum policy gradient approach. We introduce a novel quality measure that enables us to optimize the classical post-processing required for action selection, inspired by local and global quantum measurements. The resulting algorithm demonstrates a significant performance improvement in several benchmark environments. With this technique, we successfully execute a full training routine on a 5-qubit hardware device. Our method introduces only negligible classical overhead and has the potential to improve VQC-based algorithms beyond the field of quantum reinforcement learning.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 22 of its 63 references that are in the map. First posted on arXiv 13 Dec 2022.

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22 of 63 references.

References · 22

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10 more
  1. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  2. 2021Parametrized quantum policies for reinforcement learningcited by 87
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Citing papers per year: 2022: 1; 2023: 4; 2024: 10; 2025: 6.

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  1. 2023Asynchronous training of quantum reinforcement learningcited by 20
  2. 2022Quantum policy gradient algorithmscited by 15
  3. 2024Quantum Advantage Actor-Critic for Reinforcement Learningcited by 9
  4. 2025Benchmarking Quantum Reinforcement Learningcited by 9
  5. 2025A Primer on Quantum Machine Learningcited by 9
  6. 2024Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networkscited by 8
  7. 2023Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learningcited by 6
  8. 2024Warm-Start Variational Quantum Policy Iterationcited by 4
  9. 2024Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rulecited by 4
  10. 2024Trainability issues in quantum policy gradientscited by 4
  11. 2024Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularizationcited by 4
  12. 2024Fourier Analysis of Variational Quantum Circuits for Supervised Learningcited by 4
9 more
  1. 2023BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploadingcited by 3
  2. 2025Predictive Performance of Deep Quantum Data Re-uploading Modelscited by 3
  3. 2023Quantum deep Q learning with distributed prioritized experience replaycited by 2
  4. 2024Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimizationcited by 2
  5. 2025QuXAI: Explainers for Hybrid Quantum Machine Learning Modelscited by 2
  6. 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Componentscited by 2
  7. 2024Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisationcited by 1
  8. 2024On Quantum Natural Policy Gradientscited by 0
  9. 2025CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cuttingcited by 0