A Reinforcement Learning approach for Quantum State Engineering

Abstract

Machine learning (ML) has become an attractive tool in information processing, however few ML algorithms have been successfully applied in the quantum domain. We show here how classical reinforcement learning (RL) could be used as a tool for quantum state engineering (QSE). We employ a measurement based control for QSE where the action sequences are determined by the choice of the measurement basis and the reward through the fidelity of obtaining the target state. Our analysis clearly displays a learning feature in QSE, for example in preparing arbitrary two-qubit entangled states. It delivers successful action sequences, that generalise previously found human solutions from exact quantum dynamics. We provide a systematic algorithmic approach for using RL algorithms for quantum protocols that deal with non-trivial continuous state (parameter) space, and discuss on scaling of these approaches for preparation of arbitrarily large entangled (cluster) states.

10 figures

References in corpus (19)

• Adam: A Method for Stochastic Optimization
• Continuous control with deep reinforcement learning
• The nitrogen-vacancy colour centre in diamond
• A high-bias, low-variance introduction to Machine Learning for physicists
• Many-body quantum state tomography with neural networks
• High-fidelity projective readout of a solid-state spin quantum register
• Reinforcement Learning in Different Phases of Quantum Control
• Active learning machine learns to create new quantum experiments
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
• Optimizing Quantum Error Correction Codes with Reinforcement Learning
• Quantum error correction for the toric code using deep reinforcement learning
• Coherent Transport of Quantum States by Deep Reinforcement Learning
• Single Spin Magnetic Resonance
• Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator
• A neural network oracle for quantum nonlocality problems in networks
• Predicting quantum advantage by quantum walk with convolutional neural networks
• Purification of an unpolarized spin ensemble into entangled singlet pairs
• Non-classical measurement statistics induced by a coherent spin environment

Cited by in corpus (31)

• Artificial Intelligence and Machine Learning for Quantum Technologies
• Identifying optimal cycles in quantum thermal machines with reinforcement-learning
• Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
• Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
• Quantum machine learning and quantum biomimetics: A perspective
• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
• Reinforcement learning optimization of the charging of a Dicke quantum battery
• Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
• Deep reinforcement learning for quantum Hamiltonian engineering
• Dynamical learning of a photonics quantum-state engineering process
• Demonstration of a programmable optical lattice atom interferometer
• Quantum Logic Gate Synthesis as a Markov Decision Process
• 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
• Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning
• Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning
• Entanglement engineering of optomechanical systems by reinforcement learning
• 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
• Artificially intelligent Maxwell's demon for optimal control of open quantum systems
• Adaptive Random Quantum Eigensolver
• No-Collapse Accurate Quantum Feedback Control via Conditional State Tomography
• Deep learning estimation of complex reverberant wave fields by a programmable metasurface
• Quantum data generation in a denoising model with multiscale entanglement renormalization network
• Molecular Quantum Control Algorithm Design by Reinforcement Learning
• Reinforcement-learning based matterwave interferometer in a shaken optical lattice
• Bounding fidelity in quantum feedback control: theory and applications to Dicke state preparation
• Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing
• Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions