mlsanityData to 14 Oct 2026

Automatic re-calibration of quantum devices by reinforcement learning

arXiv 2404.10726 ↗First posted 16 Apr 2024quant-ph cs.LGcited by 0

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

Abstract (arXiv)

During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings. Typically, this is addressed through control loops, which monitor variables and the device performance, to maintain settings at their optimal values. Quantum devices are particularly challenging since their functionality relies on precisely tuning their parameters. At the same time, the detailed modeling of the environmental behavior is often computationally unaffordable, while a direct measure of the parameters defining the system state is costly and introduces extra noise in the mechanism. In this study, we investigate the application of reinforcement learning techniques to develop a model-free control loop for continuous recalibration of quantum device parameters. Furthermore, we explore the advantages of incorporating minimal environmental noise models. As an example, the application to numerical simulations of a Kennedy receiver-based long-distance quantum communication protocol is presented.

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 9 of its 25 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

9 of 25 references.

References · 9

  1. 2023A Definition of Continual Reinforcement Learningcited by 101
  2. 2023Challenges and Opportunities in Quantum Optimizationcited by 42
  3. 2022Equivariant quantum circuits for learning on weighted graphscited by 41
  4. 2021Domain Adaptation In Reinforcement Learning Via Latent Unified State Representationcited by 35
  5. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
  6. 2023Challenges for Reinforcement Learning in Quantum Circuit Designcited by 9
  7. 2021Quantum Optimization for Training Quantum Neural Networkscited by 6
  8. 2023Optimizing ZX-Diagrams with Deep Reinforcement Learningcited by 6
  9. 2022Enhancing Protocol Privacy with Blind Calibration of Quantum Devicescited by 1

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  3. 2025FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement
  4. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
  5. 2026Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization
  6. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  7. 2024Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
  8. 2025Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization