Quantum Many-body Calculations Using Artificial Neural Networks

Recent rapid development in the neural network architecture for machine learning and AI has been inspiring a new tool for fundamental physics research that is not even data-driven. In this article, I review one of such examples, called neural-network quantum states. Basic concepts, some of promising applications, and recent progresses for performance and wider applicability are briefly introduced, highlighting its potential as a highly competitive tool for quantum many-body simulations.