Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements

Abstract

Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in this regard. It may provide new answers to difficult questions, such as whether nonlinear measurements can compensate for linear, constrained control. Here we show that reinforcement learning can successfully discover such feedback strategies, without prior knowledge. We illustrate this for state preparation in a cavity subject to quantum-non-demolition detection of photon number, with a simple linear drive as control. Fock states can be produced and stabilized at very high fidelity. It is even possible to reach superposition states, provided the measurement rates for different Fock states can be controlled as well.

11 pages, 7 figures

References in corpus (11)

• Progressive field-state collapse and quantum non-demolition photon counting
• Quantum Non-demolition Detection of Single Microwave Photons in a Circuit
• Model-Free Quantum Control with Reinforcement Learning
• Field locked to Fock state by quantum feedback with single photon corrections
• Deep Reinforcement Learning for Quantum Gate Control
• Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer
• Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
• Quantum circuit optimization with deep reinforcement learning
• Learning feedback control strategies for quantum metrology
• Continuous measurements for control of superconducting quantum circuits
• Efficient cavity control with SNAP gates

Cited by in corpus (40)

• Artificial Intelligence and Machine Learning for Quantum Technologies
• Learning Quantum Systems
• Review: Quantum Metrology and Sensing with Many-Body Systems
• Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
• Reinforcement learning optimization of the charging of a Dicke quantum battery
• Artificial Intelligence for Quantum Computing
• Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
• Quantum Fokker-Planck Master Equation for Continuous Feedback Control
• Optimal control of families of quantum gates
• A pedagogical introduction to continuously monitored quantum systems and measurement-based feedback
• Variational quantum dynamics of two-dimensional rotor models
• Detection of entangled states supported by reinforcement learning
• Non-Markovian feedback for optimized quantum error correction
• Optimal entanglement generation in optomechanical systems via Krotov control of covariance matrix dynamics
• Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning
• Machine Learning for Estimation and Control of Quantum Systems
• Preparing Schrödinger cat states in a microwave cavity using a neural network
• Machine-learning based noise characterization and correction on neutral atoms NISQ devices
• Stabilizing two-qubit entanglement with dynamically decoupled active feedback
• Entanglement engineering of optomechanical systems by reinforcement learning
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
• Improving robustness of quantum feedback control with reinforcement learning
• A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning
• Machine learning for efficient generation of universal hybrid quantum computing resources
• Artificially intelligent Maxwell's demon for optimal control of open quantum systems
• Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• Policy Gradient Approach to Compilation of Variational Quantum Circuits
• No-Collapse Accurate Quantum Feedback Control via Conditional State Tomography
• Efficient inference of quantum system parameters by Approximate Bayesian Computation
• Quantum data generation in a denoising model with multiscale entanglement renormalization network
• Quantum state-preparation control in noisy environment via most-likely paths
• Extracting filtered signal statistics of continuously measured quantum systems
• Feedback cooling of fermionic atoms in optical lattices
• Performance Bounds for Quantum Feedback Control
• The Quantum Cartpole: A benchmark environment for non-linear reinforcement learning
• Bounding fidelity in quantum feedback control: theory and applications to Dicke state preparation