I will discuss how machine learning can be used to alleviate the sign problem in stochastic simulations of low-D systems. The method we use is based off neural network (NN) approximations of Lefschetz thimbles that are determined via holomorphic flow. The target Hamiltonian is the Hubbard model, but our application can be adapted to other systems. I provide results for non-bipartite systems...
The ability to efficiently draw independent configurations from a general density function is a major computational challenge that has been studied extensively across a variety of scientific disciplines. In particular, for High Energy Physics, the effort required to generate independent gauge field configurations is known to scale exponentially as we approach physical lattice volumes.
We...
I will discuss our recent advancement on neural network quantum states with gauge theories for quantum lattice models. I will first introduce the gauge equivariant neural-network quantum states for quantum lattice gauge theories with Zd gauge group and non-abelian Kitaev D(G) models. In particular, the neural network representation is combined with variational quantum Monte Carlo to...