Improving robustness of quantum feedback control with reinforcement learning
arXiv:2401.17190 · doi:10.1103/PhysRevA.110.012605
Abstract
Obtaining reliable state preparation protocols is a key step towards practical implementation of many quantum technologies, and one of the main tasks in quantum control. In this work, different reinforcement learning approaches are used to derive a feedback law for state preparation of a desired state in a target system. In particular, we focus on the robustness of the obtained strategies with respect to different types and amount of noise. Comparing the results indicates that the learned controls are more robust to unmodeled perturbations with respect to simple feedback strategy based on optimized population transfer, and that training on simulated nominal model retain the same advantages displayed by controllers trained on real data. The possibility of effective off-line training of robust controllers promises significant advantages towards practical implementation.
References in corpus (32)
- Quantum control theory and applications: A survey
- Real-time quantum feedback prepares and stabilizes photon number states
- Real-time quantum error correction beyond break-even
- Quantum feedback control and classical control theory
- Coherent quantum LQG control
- Reinforcement Learning in Different Phases of Quantum Control
- Quantum feedback: theory, experiments, and applications
- Modeling and Control of Quantum Systems: An Introduction
- Reinforcement Learning with Neural Networks for Quantum Feedback
- Stabilizing feedback controls for quantum systems
- Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
- Optimizing Quantum Error Correction Codes with Reinforcement Learning
- Model-Free Quantum Control with Reinforcement Learning
- When does reinforcement learning stand out in quantum control? A comparative study on state preparation
- Quantum error correction for the toric code using deep reinforcement learning
- A discrete invitation to quantum filtering and feedback control
- Coherent Transport of Quantum States by Deep Reinforcement Learning
- Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
- Quantum POMDPs
- Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
- Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator
- A Reinforcement Learning approach for Quantum State Engineering
- Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
- Reinforcement Learning for Digital Quantum Simulation
- Faster State Preparation across Quantum Phase Transition Assisted by Reinforcement Learning
- Hamiltonian Control of Quantum Dynamical Semigroups: Stabilization and Convergence Speed
- Classifying global state preparation via deep reinforcement learning
- Engineering Stable Discrete-Time Quantum Dynamics via a Canonical QR Decomposition
- Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
- Dissipative Feedback Switching for Quantum Stabilization
- Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
- Deep reinforcement learning for preparation of thermal and prethermal quantum states