Machine learning quantum states and many-body entanglement

Recently, machine learning techniques have been introduced to many-body quantum condensed matter physics, raising considerable interest across different communities. In this talk, I will briefly introduce a neural-network representation of quantum many-body states and show that this representation can describe certain topological states in an exact and efficient fashion. I will talk about the entanglement properties, such as entanglement entropy and spectrum, of those quantum states that can be represented efficiently by neural networks. I will also show that neural networks can be used, through reinforcement learning, to solve a challenging problem of calculating the massively entangled ground state for a model Hamiltonian with long-range interactions.