# Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control

arXiv: https://arxiv.org/abs/2408.11328 · HTML: https://mlsanity.com/paper/2408.11328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Placed in

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

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling](https://mlsanity.com/paper/1906.03393/) (2019)
- [Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving](https://mlsanity.com/paper/2010.03655/) (2020)
- [Sampling-based Learning Control for Quantum Systems with Uncertainties](https://mlsanity.com/paper/1507.07190/) (2015)
- [Quantum estimation, control and learning: opportunities and challenges](https://mlsanity.com/paper/2201.05835/) (2022)
- [Rapid Lyapunov control of finite-dimensional quantum systems](https://mlsanity.com/paper/1512.02545/) (2015)
- [Two-step feedback preparation of entanglement for qubit systems with time delay](https://mlsanity.com/paper/2307.14599/) (2023)

## Built on by (most cited first, 1 of 1)

- [Riemannian Optimization for Multi-Player Quantum Games on Product Unitary Manifolds](https://mlsanity.com/paper/2609.07397/) (2026)

## Abstract (arXiv)

The stabilization of quantum states is a fundamental problem for realizing various quantum technologies. Measurement-based-feedback strategies have demonstrated powerful performance, and the construction of quantum control signals using measurement information has attracted great interest. However, the interaction between quantum systems and the environment is inevitable, especially when measurements are introduced, which leads to decoherence. To mitigate decoherence, it is desirable to stabilize quantum systems faster, thereby reducing the time of interaction with the environment. In this paper, we utilize information obtained from measurement and apply deep reinforcement learning (DRL) algorithms, without explicitly constructing specific complex measurement-control mappings, to rapidly drive random initial quantum state to the target state. The proposed DRL algorithm has the ability to speed up the convergence to a target state, which shortens the interaction between quantum systems and their environments to protect coherence. Simulations are performed on two-qubit and three-qubit systems, and the results show that our algorithm can successfully stabilize random initial quantum system to the target entangled state, with a convergence time faster than traditional methods such as Lyapunov feedback control and several DRL algorithms with different reward functions. Moreover, it exhibits robustness against imperfect measurements and delays in system evolution.