# Automatic re-calibration of quantum devices by reinforcement learning

arXiv: https://arxiv.org/abs/2404.10726 · HTML: https://mlsanity.com/paper/2404.10726/

## 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.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 9 of 9)

- [A Definition of Continual Reinforcement Learning](https://mlsanity.com/paper/2307.11046/) (2023)
- [Challenges and Opportunities in Quantum Optimization](https://mlsanity.com/paper/2312.02279/) (2023)
- [Equivariant quantum circuits for learning on weighted graphs](https://mlsanity.com/paper/2205.06109/) (2022)
- [Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation](https://mlsanity.com/paper/2102.05714/) (2021)
- [Quantum anomaly detection in the latent space of proton collision events at the LHC](https://mlsanity.com/paper/2301.10780/) (2023)
- [Challenges for Reinforcement Learning in Quantum Circuit Design](https://mlsanity.com/paper/2312.11337/) (2023)
- [Quantum Optimization for Training Quantum Neural Networks](https://mlsanity.com/paper/2103.17047/) (2021)
- [Optimizing ZX-Diagrams with Deep Reinforcement Learning](https://mlsanity.com/paper/2311.18588/) (2023)
- [Enhancing Protocol Privacy with Blind Calibration of Quantum Devices](https://mlsanity.com/paper/2209.05634/) (2022)

## 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.