Inferring the quantum density matrix with machine learning

In particular, There is a nexus of concepts at the heart of a rich interplay machine learning techniques have been used for between physics, statistics, machine learning, and variational optimization of ground state energy for quantum information theory. Concepts such as entropy that were systems [6]. Additionally, there have been a number key to the early work in thermodynamics are the bedrock of important developments that extend statistical inference of information theory. Similarly the Gibbs (or Boltzman) to domains where probabilistic modeling was previously distribution, which characterize the distribution of states inaccessible. These techniques have recently been in thermal equilibrium, is at the heart of energy based explored to solve statistical mechanics of classical systems models and Boltzman machines that were widely studied [7, 8]. In this work, we aim to connect recent developments in machine learning [1, 2]. Additionally, the study of in deep generative models [9-12], unsupervised complicated many-body systems gave rise to mean-field learning for implicit models [13], and variational inference methods and renormalization group methods.