# Reinforcement Learning for Quantum Technology

arXiv: https://arxiv.org/abs/2601.18953 · HTML: https://mlsanity.com/paper/2601.18953/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- First posted on arXiv 26 Jan 2026.

## Placed in

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

## Built on by (most cited first, 6 of 6)

- [Replay-buffer engineering for noise-aware quantum circuit optimization](https://mlsanity.com/paper/2604.21863/) (2026)
- [Learning Unified Control of Intrinsic Nonlinear Spin Dynamics in Atomic Qudits for Magnetometry](https://mlsanity.com/paper/2603.28421/) (2026)
- [Reinforcement learning for ion shuttling on trapped-ion quantum computers](https://mlsanity.com/paper/2605.22463/) (2026)
- [Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization](https://mlsanity.com/paper/2605.26925/) (2026)
- [Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning](https://mlsanity.com/paper/2607.09422/) (2026)
- [Generative Replay Mitigates Sample Starvation in Quantum Architecture Search](https://mlsanity.com/paper/2609.11248/) (2026)

## Abstract (arXiv)

Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.