mlsanityData to 14 Oct 2026

Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components

arXiv 2511.17112 ↗First posted 21 Nov 2025quant-ph cs.LGcited by 2

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

Abstract (arXiv)

Parameterised quantum circuit (PQC) based Quantum Reinforcement Learning (QRL) has emerged as a promising paradigm at the intersection of quantum computing and reinforcement learning (RL). By design, PQCs create hybrid quantum-classical models, but their practical applicability remains uncertain due to training instabilities, barren plateaus (BPs), and the difficulty of isolating the contribution of individual pipeline components. In this work, we dissect PQC based QRL architectures through a systematic experimental evaluation of three aspects recurrently identified as critical: (i) data embedding strategies, with Data Reuploading (DR) as an advanced approach; (ii) ansatz design, particularly the role of entanglement; and (iii) post-processing blocks after quantum measurement, with a focus on the underexplored Output Reuse (OR) technique. Using a unified PPO-CartPole framework, we perform controlled comparisons between hybrid and classical agents under identical conditions. Our results show that OR, though purely classical, exhibits distinct behaviour in hybrid pipelines, that DR improves trainability and stability, and that stronger entanglement can degrade optimisation, offsetting classical gains. Together, these findings provide controlled empirical evidence of the interplay between quantum and classical contributions, and establish a reproducible framework for systematic benchmarking and component-wise analysis in QRL.

Key facts. Cited by 2 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 18 of its 35 references that are in the map. First posted on arXiv 21 Nov 2025.

Builds on

18 of 35 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024A quantum-classical reinforcement learning model to play Atari games11 Dec
  2. 2024Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization28 Oct
  3. 2024Trainability issues in quantum policy gradients13 Jun
  4. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning20 May
  5. 2024Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization13 May
  6. 2024VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability21 Jan
  7. 2024Quantum Advantage Actor-Critic for Reinforcement Learning13 Jan

Other references · 11

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  3. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  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 Deep Reinforcement Learning for Robot Navigation Taskscited by 16
  9. 2022Automated Quantum Circuit Design with Nested Monte Carlo Tree Searchcited by 16
  10. 2022Policy Gradients using Variational Quantum Circuitscited by 15
  11. 2022Variational Quantum Soft Actor-Critic for Robotic Arm Controlcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Quantum entanglement provides a competitive advantage in adversarial gamescited by 0
  2. 2026Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  2. 2026Reinforcement Learning for Quantum Technology
  3. 2025Quantum Circuit Structure Optimization for Quantum Reinforcement Learning
  4. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning
  5. 2025Quantum Reinforcement Learning by Adaptive Non-local Observables
  6. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
  7. 2025Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
  8. 2025Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP