This chapter explores the behavior of random walks on graphs, framed within the broader context of Markov chains. It introduces finite-state Markov chains, explaining key concepts such as transition matrices, the Markov property, and the computation of stationary distributions. The chapter then discusses the long-term behavior of Markov chains, including the convergence to equilibrium under conditions of irreducibility and aperiodicity. The chapter delves into the application of random walks on graphs, particularly in the context of PageRank, a method for identifying central nodes in a network. It also discusses Markov chain Monte Carlo (MCMC) methods, specifically the Metropolis–Hastings algorithm and Gibbs sampling, which are used to generate samples from complex probability distributions. The chapter concludes by illustrating the application of Gibbs sampling to generate images of handwritten digits using a restricted Boltzmann machine.

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.