mlsanityData to 14 Oct 2026

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

arXiv 2408.11328 ↗First posted 21 Aug 2024eess.SY cs.SYcited by 1

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

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.

Key facts. Cited by 1 paper 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.

Builds on

10 of 48 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  4. 2017Inverse Reward Designcited by 249
  5. 2019Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Samplingcited by 119
  6. 2020Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Drivingcited by 6
  7. 2015Sampling-based Learning Control for Quantum Systems with Uncertaintiescited by 3
  8. 2022Quantum estimation, control and learning: opportunities and challengescited by 3
  9. 2015Rapid Lyapunov control of finite-dimensional quantum systemscited by 2
  10. 2023Two-step feedback preparation of entanglement for qubit systems with time delaycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Riemannian Optimization for Multi-Player Quantum Games on Product Unitary Manifoldscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Model-free Distortion Canceling and Control of Quantum Devices
  2. 2026Reinforcement Learning for Quantum Technology
  3. 2024Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations
  4. 2026Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning
  5. 2026Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization
  6. 2024Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
  7. 2026Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
  8. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis