Artificial Intelligence and Machine Learning for Quantum Technologies

Abstract

In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.

arXiv comment23 pages, 8 figures; comments welcome!

References in corpus (106)

• Deep Learning in Neural Networks: An Overview
• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum Machine Learning
• Variational Quantum Algorithms
• Quantum Simulation
• Machine learning and the physical sciences
• Barren plateaus in quantum neural network training landscapes
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Noisy intermediate-scale quantum (NISQ) algorithms
• Machine learning phases of matter
• An introduction to quantum machine learning
• A high-bias, low-variance introduction to Machine Learning for physicists
• Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
• GuacaMol: Benchmarking Models for De Novo Molecular Design
• Many-body quantum state tomography with neural networks
• Learning phase transitions by confusion
• Photonic quantum information processing: a review
• Entanglement-free Heisenberg-limited phase estimation
• Quantum advantage in learning from experiments
• Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
• Optimal control technique for Many Body Quantum Systems dynamics
• On scientific understanding with artificial intelligence
• Reinforcement Learning in Different Phases of Quantum Control
• Active learning machine learns to create new quantum experiments
• Optimal states and almost optimal adaptive measurements for quantum interferometry
• Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
• Learning the quantum algorithm for state overlap
• Experimental Quantum Hamiltonian Learning
• Automated Search for new Quantum Experiments
• Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
• Variational neural network ansatz for steady states in open quantum systems
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Hamiltonian Learning and Certification Using Quantum Resources
• Machine Learning for Precise Quantum Measurement
• Graph-theoretic Simplification of Quantum Circuits with the ZX-calculus
• A Neural Decoder for Topological Codes
• Constructing neural stationary states for open quantum many-body systems
• Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
• Optimizing Quantum Error Correction Codes with Reinforcement Learning
• Machine Learning for Quantum Matter
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Model-Free Quantum Control with Reinforcement Learning
• Machine learning for discriminating quantum measurement trajectories and improving readout
• Speedup for quantum optimal control from automatic differentiation based on graphics processing units
• Deep Reinforcement Learning for Quantum Gate Control
• Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer
• Decoding Small Surface Codes with Feedforward Neural Networks
• Machine learning for long-distance quantum communication
• Unsupervised phase discovery with deep anomaly detection
• Identifying optimal cycles in quantum thermal machines with reinforcement-learning
• Machine learning of noise-resilient quantum circuits
• Deep Neural Network Probabilistic Decoder for Stabilizer Codes
• Machine learning quantum states in the NISQ era
• An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
• Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes
• Topological Quantum Compiling with Reinforcement Learning
• Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
• Generalizable control for quantum parameter estimation through reinforcement learning
• Neural Predictor based Quantum Architecture Search
• Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Computer-inspired Quantum Experiments
• Computer-inspired concept for high-dimensional multipartite quantum gates
• Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
• Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
• Neural network quantum state tomography in a two-qubit experiment
• Unsupervised machine learning of topological phase transitions from experimental data
• Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
• Automatic spin-chain learning to explore the quantum speed limit
• Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
• Differential Evolution for Many-Particle Adaptive Quantum Metrology
• Automated tuning of double quantum dots into specific charge states using neural networks
• Deep Reinforcement Learning Control of Quantum Cartpoles
• Experimental quantum homodyne tomography via machine learning
• Learning models of quantum systems from experiments
• Genetic Algorithms for Digital Quantum Simulations
• Calibration of quantum sensors by neural networks
• Machine Learning meets Quantum Foundations: A Brief Survey
• Efficiently measuring a quantum device using machine learning
• A Reinforcement Learning approach for Quantum State Engineering
• Scalable Neural Decoder for Topological Surface Codes
• A search algorithm for quantum state engineering and metrology
• Universal gates for protected superconducting qubits using optimal control
• Quantum machine learning and quantum biomimetics: A perspective
• A differentiable programming method for quantum control
• Conceptual understanding through efficient inverse-design of quantum optical experiments
• Improving the dynamics of quantum sensors with reinforcement learning
• Pattern recognition techniques for Boson Sampling validation
• Machine learning assisted readout of trapped-ion qubits
• Natural Evolutionary Strategies for Variational Quantum Computation
• Designing quantum experiments with a genetic algorithm
• Neural networks for detecting multimode Wigner-negativity
• Learning quantum dynamics with latent neural ODEs
• Phase Detection with Neural Networks: Interpreting the Black Box
• Machine Learning and Quantum Devices
• Quantum Computer-Aided design of Quantum Optics Hardware
• Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
• Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
• Deep Learning of Quantum Many-Body Dynamics via Random Driving
• Control of Stochastic Quantum Dynamics by Differentiable Programming
• Design of quantum optical experiments with logic artificial intelligence
• Quantum computer-aided design: digital quantum simulation of quantum processors
• The Learnability of Quantum States
• Genetic braid optimization: A heuristic approach to compute quasiparticle braids

Cited by in corpus (56)

• Learning Quantum Systems
• Review: Quantum Metrology and Sensing with Many-Body Systems
• Quantum Cloud Computing: Trends and Challenges
• Building a Quantum-ready Ecosystem
• Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
• Artificial intelligence discovery of a charging protocol in a micromaser quantum battery
• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
• Artificial Intelligence for Quantum Computing
• Recent Advances in Deep Learning for Channel Coding: A Survey
• Quantum circuit synthesis with diffusion models
• Digital Discovery of 100 diverse Quantum Experiments with PyTheus
• Supervised learning for robust quantum control in composite-pulse systems
• Quantum Computing: Vision and Challenges
• Spectral Density Classification For Environment Spectroscopy
• Deep learning the hierarchy of steering measurement settings of qubit-pair states
• Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
• Non-Markovian feedback for optimized quantum error correction
• A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems
• Reinforcement learning pulses for transmon qubit entangling gates
• Qutrit Circuits and Algebraic Relations: A Pathway to Efficient Spin-1 Hamiltonian Simulation
• Forecasting high-impact research topics via machine learning on evolving knowledge graphs
• Preparing Schrödinger cat states in a microwave cavity using a neural network
• Why business adoption of quantum and AI technology must be ethical
• Fast generation of entanglement between coupled spins using optimization and deep learning methods
• Cavity-Heisenberg spin-$j$ chain quantum battery and reinforcement learning optimization
• Designing fast quantum gates using optimal control with a reinforcement-learning ansatz
• Gradient-descent methods for fast quantum state tomography
• Generation of genuine multipartite entangled states via indistinguishability of identical particles
• Framework for Learning and Control in the Classical and Quantum Domains
• Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
• Characterization of a driven two-level quantum system by Supervised Learning
• Noise Classification in Three-Level Quantum Networks by Machine Learning
• Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
• Preparation of conditionally-squeezed states in qubit-oscillator systems
• Supervised Learning Guarantee for Quantum AdaBoost
• The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
• Artificially intelligent Maxwell's demon for optimal control of open quantum systems
• Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
• Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach
• Machine learning non-Markovian two-level quantum noise spectroscopy
• Revealing molecule-internal mechanisms that control phonon heat transport through single-molecule junctions by a genetic algorithm
• Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning
• Unveiling the nonclassicality within quasi-distribution representations through deep learning
• Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
• Learning a quantum computer's capability
• Learning the dynamics of Markovian open quantum systems from experimental data
• Artificial intelligence for representing and characterizing quantum systems
• Exploring exotic configurations with anomalous features using deep learning: Application of classical and quantum-classical hybrid anomaly detection
• Many-Body Neural Network Wavefunction for a Non-Hermitian Ising Chain
• Quantum dynamics evolution predicted by the long short-term memory network in the photosystem II reaction center
• Data-Driven Reconstruction and Characterization of Stochastic Dynamics via Dynamical Mode Decomposition
• Bounding fidelity in quantum feedback control: theory and applications to Dicke state preparation