Machine learning spatial geometry from entanglement features

Machine learning is a fast developing area that finds applications in all disciplines of science. Here, the authors demonstrate that the machine learning (in particular deep learning) technique can be applied to understand the emergence of spatial geometry from learning the features of quantum many-body entanglement, an idea that was proposed in a recent study of the holography duality in quantum gravity. This work is the first to successfully demonstrate the idea of ``geometry emerging from learning''.