# Deep Reinforcement Learning Control of Quantum Cartpoles

arXiv: https://arxiv.org/abs/1910.09200 · HTML: https://mlsanity.com/paper/1910.09200/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Variational Quantum Soft Actor-Critic](https://mlsanity.com/paper/2112.11921/) (2021)
- [Convergent and Efficient Deep Q Network Algorithm](https://mlsanity.com/paper/2106.15419/) (2021)
- [Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions](https://mlsanity.com/paper/2106.01779/) (2021)
- [Active Learning for the Optimal Design of Multinomial Classification in Physics](https://mlsanity.com/paper/2109.08612/) (2021)
- [Deep learning enhanced Rydberg multifrequency microwave recognition](https://mlsanity.com/paper/2202.13617/) (2022)
- [Reinforcement learning to learn quantum states for Heisenberg scaling accuracy](https://mlsanity.com/paper/2412.02334/) (2024)
- [Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement Learning](https://mlsanity.com/paper/2212.10705/) (2022)
- [Topological Learning in Multi-Class Data Sets](https://mlsanity.com/paper/2301.09734/) (2023)
- [QF-tuner: Breaking Tradition in Reinforcement Learning](https://mlsanity.com/paper/2402.16562/) (2024)
- [Model-free Distortion Canceling and Control of Quantum Devices](https://mlsanity.com/paper/2407.09877/) (2024)

## Abstract (arXiv)

We generalize a standard benchmark of reinforcement learning, the classical cartpole balancing problem, to the quantum regime by stabilizing a particle in an unstable potential through measurement and feedback. We use state-of-the-art deep reinforcement learning to stabilize a quantum cartpole and find that our deep learning approach performs comparably to or better than other strategies in standard control theory. Our approach also applies to measurement-feedback cooling of quantum oscillators, showing the applicability of deep learning to general continuous-space quantum control.