mlsanityData to 14 Oct 2026

From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware

arXiv 2509.07614 ↗First posted 9 Sep 2025quant-ph cs.AIcited by 0

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

Abstract (arXiv)

Quantum policy evaluation (QPE) is a reinforcement learning (RL) algorithm which is quadratically more efficient than an analogous classical Monte Carlo estimation. It makes use of a direct quantum mechanical realization of a finite Markov decision process, in which the agent and the environment are modeled by unitary operators and exchange states, actions, and rewards in superposition. Previously, the quantum environment has been implemented and parametrized manually for an illustrative benchmark using a quantum simulator. In this paper, we demonstrate how these environment parameters can be learned from a batch of classical observational data through quantum machine learning (QML) on quantum hardware. The learned quantum environment is then applied in QPE to also compute policy evaluations on quantum hardware. Our experiments reveal that, despite challenges such as noise and short coherence times, the integration of QML and QPE shows promising potential for achieving quantum advantage in RL.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 24 references that are in the map. First posted on arXiv 9 Sep 2025.

Builds on

12 of 24 references.

References · 12

  1. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  2. 2021Parametrized quantum policies for reinforcement learningcited by 90
  3. 2016Quantum-enhanced machine learningcited by 79
  4. 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
  5. 2022A Survey on Quantum Reinforcement Learningcited by 53
  6. 2021Quantum Algorithms for Reinforcement Learning with a Generative Modelcited by 24
  7. 2022Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learningcited by 21
  8. 2022Quantum Reinforcement Learning via Policy Iterationcited by 19
  9. 2015Framework for learning agents in quantum environmentscited by 9
  10. 2020Quantum Circuit Evolution on NISQ Devicescited by 9
  11. 2017Exponential improvements for quantum-accessible reinforcement learningcited by 7
  12. 2024Model-based Offline Quantum Reinforcement Learningcited by 4

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  1. 2025Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
  2. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
  3. 2024An Introduction to Quantum Reinforcement Learning (QRL)
  4. 2026Reinforcement Learning for Quantum Technology
  5. 2026Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
  6. 2025Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
  7. 2026QnRL: Quantum-Native Reinforcement Learning
  8. 2026Quantum vs. Classical Machine Learning: A Unified Empirical Comparison