mlsanityData to 14 Oct 2026

Warm-Start Variational Quantum Policy Iteration

arXiv 2404.10546 ↗First posted 16 Apr 2024quant-ph cs.LGcited by 4

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iteration, which requires to solve a typically large linear system of equations. We propose the variational quantum policy iteration (VarQPI) algorithm, realizing this step with a NISQ-compatible quantum-enhanced subroutine. Its scalability is supported by an analysis of the structure of generic reinforcement learning environments, laying the foundation for potential quantum advantage with utility-scale quantum computers. Furthermore, we introduce the warm-start initialization variant (WS-VarQPI) that significantly reduces resource overhead. The algorithm solves a large FrozenLake environment with an underlying 256x256-dimensional linear system, indicating its practical robustness.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 34 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

10 of 34 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016OpenAI Gymcited by 1,736
  3. 2020Variational Quantum Algorithmscited by 692
  4. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  5. 2021Parametrized quantum policies for reinforcement learningcited by 90
  6. 2020Quantum reinforcement learning in continuous action spacecited by 29
  7. 2022Quantum Policy Gradient Algorithm with Optimized Action Decodingcited by 21
  8. 2022Quantum Reinforcement Learning via Policy Iterationcited by 19
  9. 2023Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learningcited by 6
  10. 2024Comprehensive Library of Variational LSE Solverscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularizationcited by 4
  2. 2025Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correctioncited by 1
  3. 2026Learning to Concatenate Quantum Codescited by 1
  4. 2026Learning Logical Operations for Arbitrary Quantum Error Correction Codescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuits
  2. 2025Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
  3. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
  4. 2025Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
  5. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
  6. 2025Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments
  7. 2026Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
  8. 2026Reinforcement Learning for Quantum Technology