mlsanityData to 14 Oct 2026

Quantum Reinforcement Learning by Adaptive Non-local Observables

arXiv 2507.19629 ↗First posted 25 Jul 2025quant-ph cs.AI cs.LGcited by 5

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

Abstract (arXiv)

Hybrid quantum-classical frameworks leverage quantum computing for machine learning; however, variational quantum circuits (VQCs) are limited by the need for local measurements. We introduce an adaptive non-local observable (ANO) paradigm within VQCs for quantum reinforcement learning (QRL), jointly optimizing circuit parameters and multi-qubit measurements. The ANO-VQC architecture serves as the function approximator in Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms. On multiple benchmark tasks, ANO-VQC agents outperform baseline VQCs. Ablation studies reveal that adaptive measurements enhance the function space without increasing circuit depth. Our results demonstrate that adaptive multi-qubit observables can enable practical quantum advantages in reinforcement learning.

Key facts. Cited by 5 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 19 of its 34 references that are in the map. First posted on arXiv 25 Jul 2025.

Builds on

19 of 34 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Quantum Advantage Actor-Critic for Reinforcement Learning13 Jan

Other references · 18

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2020Variational Quantum Algorithmscited by 692
  4. 2018Supervised learning with quantum enhanced feature spacescited by 665
  5. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  6. 2019Parameterized quantum circuits as machine learning modelscited by 332
  7. 2016Quantum Machine Learningcited by 326
  8. 2017Noisy Networks for Explorationcited by 266
  9. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  10. 2021Parametrized quantum policies for reinforcement learningcited by 90
  11. 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
  12. 2022A Survey on Quantum Reinforcement Learningcited by 53
6 more
  1. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  2. 2021Quantum machine learning of large datasets using randomized measurementscited by 30
  3. 2023Asynchronous training of quantum reinforcement learningcited by 20
  4. 2025Learning to Measure Quantum Neural Networkscited by 15
  5. 2025Adaptive Non-local Observable on Quantum Neural Networkscited by 7
  6. 2025Learning to Program Quantum Measurements for Machine Learningcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Multivariate Time Series Forecasting with Adaptive Non-Local Observablescited by 1
  2. 2026Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimizationcited by 0
  3. 2026Diagonal Adaptive Non-local Observables on Quantum Neural Networkscited by 0
  4. 2026Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functionscited by 0
  5. 2026Learning to Program Adaptive Non-Local Observables for Machine Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
  2. 2025Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
  3. 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components
  4. 2024Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization
  5. 2026Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
  6. 2024VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
  7. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning
  8. 2024Reinforcement Learning for Variational Quantum Circuits Design