Neural-network quantum states

Cambridge University Press eBooks, vol. 2018, pp. 111–144

Abstract

In this chapter, we review the growing field of research aiming to represent quantum states with machine learning models, known as neural quantum states. We introduce the key ideas and methods and review results about the capacity of such representations. We discuss in details many applications of neural quantum states, including but not limited to finding the ground state of a quantum system, solving its time evolution equation, quantum tomography, open quantum system dynamics and steady-state solution, and quantum chemistry. Finally, we discuss the challenges to be solved to fully unleash the potential of neural quantum states.

Authors 2

1. Borja Requena

   Institute of Photonic Sciences

   Affiliation as printed

   ICFO - The Institute of Photonic Sciences

2. Marylou Gabrié

   Institut Polytechnique de Paris

   Affiliation as printed

   Institut Polytechnique de Paris