Machine learning meets quantum physics

Abstract

The marriage of machine learning and quantum physics may give birth to a new research frontier that could transform both.

The following article appeared in Physics Today, March 2019, page 48

References in corpus (12)

• Quantum Machine Learning
• The density-matrix renormalization group in the age of matrix product states
• Quantum algorithm for solving linear systems of equations
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Learning phase transitions by confusion
• Discovering Phase Transitions with Unsupervised Learning
• Quantum machine learning: a classical perspective
• Quantum Entanglement in Neural Network States
• Efficient Representation of Quantum Many-body States with Deep Neural Networks
• Machine Learning Topological States
• Machine Learning Bell Nonlocality in Quantum Many-body Systems

Cited by in corpus (71)

• All Optical Neural Network with Nonlinear Activation Functions
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Unsupervised machine learning and band topology
• Quantum Adversarial Machine Learning
• A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
• Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics
• Quantum federated learning through blind quantum computing
• Experimental quantum adversarial learning with programmable superconducting qubits
• Topological Quantum Compiling with Reinforcement Learning
• Recent advances for quantum classifiers
• Transfer learning for scalability of neural-network quantum states
• Markovian Quantum Neuroevolution for Machine Learning
• The Presence and Absence of Barren Plateaus in Tensor-network Based Machine Learning
• Quantum Neural Network Classifiers: A Tutorial
• Realizing a quantum generative adversarial network using a programmable superconducting processor
• Deep quantum neural networks equipped with backpropagation on a superconducting processor
• Solving Quasiparticle Band Spectra of Real Solids using Neural-Network Quantum States
• Unsupervised Learning of Non-Hermitian Topological Phases
• Deep learning stochastic processes with QCD phase transition
• Neural network setups for a precise detection of the many-body localization transition: finite-size scaling and limitations
• Non-IID Quantum Federated Learning with One-shot Communication Complexity
• Quantum topology identification with deep neural networks and quantum walks
• A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics
• Learning quantum dynamics with latent neural ODEs
• Generative models for sampling and phase transition indication in spin systems
• Deep Quantum Geometry of Matrices
• Quantum Capsule Networks
• Artificial Neural Network Based Computation for Out-of-Time-Ordered Correlators
• Challenges and perspectives for new material solutions in batteries
• Machine learning nonequilibrium electron forces for adiabatic spin dynamics
• Single-preparation unsupervised quantum machine learning: concepts and applications
• Relevance in the Renormalization Group and in Information Theory
• Demonstration of low-overhead quantum error correction codes
• Quantum reinforcement learning in continuous action space
• Protocol for implementing quantum nonparametric learning with trapped ions
• ScQ cloud quantum computation for generating Greenberger-Horne-Zeilinger states of up to 10 qubits
• Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions
• Machine classification for probe based quantum thermometry
• Machine learning predictions for local electronic properties of disordered correlated electron systems
• Quantum Continual Learning Overcoming Catastrophic Forgetting
• Neural-network-based parameter estimation for quantum detection
• Quantum process tomography with unknown single-preparation input states
• Experimental demonstration of adversarial examples in learning topological phases
• Direct implementation of a perceptron in superconducting circuit quantum hardware
• Entropy Regularized Reinforcement Learning Using Large Deviation Theory
• Morphology of three-body quantum states from machine learning
• Efficient neural-network based variational Monte Carlo scheme for direct optimization of excited energy states in frustrated quantum systems
• Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
• Random Sampling Neural Network for Quantum Many-Body Problems
• Quantum continual learning on a programmable superconducting processor
• Adversarial Machine Learning Phases of Matter
• Entropy, Free Energy, and Work of Restricted Boltzmann Machines
• Minimization of ion micromotion with artificial neural network
• Quantum delegated and federated learning via quantum homomorphic encryption
• Universal adversarial perturbations for multiple classification tasks with quantum classifiers
• Quantum System Compression: A Hamiltonian Guided Walk Through Hilbert Space
• Quantum Inception Score
• State Classification via a Random-Walk-Based Quantum Neural Network
• Vicsek Model Meets DBSCAN: Cluster Phases in the Vicsek Model
• Quantum Compiling by Deep Reinforcement Learning
• Many-body mobility edges in 1D and 2D revealed by convolutional neural networks
• Considerations in the use of ML interaction potentials for free energy calculations
• Quantum circuit complexity and unsupervised machine learning of topological order
• Classifying topological neural network quantum states via diffusion maps
• Research progress of artificial intelligence empowered quantum communication and quantum sensing systems
• Artificial intelligence for representing and characterizing quantum systems
• Estimating the degree of non-Markovianity using variational quantum circuits
• Efficiency of neural-network state representations of one-dimensional quantum spin systems
• Decoding Quantum Search Advantage: The Critical Role of State Properties in Random Walks
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• Adaptive Bayesian algorithm for achieving desired quantum transition