mlsanityData to 14 Oct 2026

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning

arXiv 2607.09422 ↗First posted 10 Jul 2026cs.LG cond-mat.mes-hallcited by 0

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Abstract (arXiv)

Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays. However, if parameter cross-talk is strong, a non-stationary environment from the perspective of any individual agent can destabilize learning - the same effect that plagues manual tuning of such systems. We propose using a factored representation of the action space, learned online, to decouple agents and minimize their interference. Our framework, QADAPT, uses this factorization to efficiently learn shared policies based on local measurements and rewards. With this modular strategy, we achieve zero-shot generalization to unseen quantum device sizes and maintain an approximately constant number of convergence steps to reach target regimes. This work provides a scalable route toward the rapid calibration of large-scale quantum processors.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 42 of its 91 references that are in the map. First posted on arXiv 10 Jul 2026.

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42 of 91 references.

References · 42

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2015Continuous control with deep reinforcement learningcited by 3,407
  5. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  6. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  7. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  8. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  9. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  10. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 379
  11. 2016Learning Multiagent Communication with Backpropagationcited by 366
  12. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
30 more
  1. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 186
  2. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  3. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 155
  4. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  5. 2020Learning to Utilize Shaping Rewards: A New Approach of Reward Shapingcited by 114
  6. 2021Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharingcited by 87
  7. 2023Heterogeneous-Agent Reinforcement Learningcited by 69
  8. 2018Experience-driven Networking: A Deep Reinforcement Learning based Approachcited by 41
  9. 2022Scaling Laws for a Multi-Agent Reinforcement Learning Modelcited by 38
  10. 2022Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregationcited by 36
  11. 2019Graph Policy Gradients for Large Scale Robot Controlcited by 28
  12. 2020Machine learning enables completely automatic tuning of a quantum device faster than human expertscited by 23
  13. 2022Tuning arrays with rays: Physics-informed tuning of quantum dot charge statescited by 16
  14. 2020Deep Reinforcement Learning for Efficient Measurement of Quantum Devicescited by 15
  15. 2020Quantum device fine-tuning using unsupervised embedding learningcited by 14
  16. 2024Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Taskscited by 13
  17. 2024HyperMARL: Adaptive Hypernetworks for Multi-Agent RLcited by 11
  18. 2026QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understandingcited by 11
  19. 2018Multi-agent Deep Reinforcement Learning with Extremely Noisy Observationscited by 10
  20. 2021Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learningcited by 7
  21. 2024Fully autonomous tuning of a spin qubitcited by 7
  22. 2023Data needs and challenges for quantum dot devices automationcited by 6
  23. 2024Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learningcited by 6
  24. 2026Reinforcement Learning for Quantum Technologycited by 6
  25. 2024Quantum feedback control with a transformer neural network architecturecited by 5
  26. 2025MetaSym: A Symplectic Meta-learning Framework for Physical Intelligencecited by 5
  27. 2025Meta-learning characteristics and dynamics of quantum systemscited by 4
  28. 2017Nonlinear Bayesian Estimation: From Kalman Filtering to a Broader Horizoncited by 3
  29. 2025End-to-End Analysis of Charge Stability Diagrams with Transformerscited by 3
  30. 2026Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensuscited by 2

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