Sampling the Eigenstates of an Infinite Dimensional Matrix with Rimu.jl

Explore quantum many-body problems through a conference talk demonstrating how to sample eigenstates of infinite dimensional matrices using the Rimu.jl package. Learn about the challenges of working with Hilbert spaces of infinite dimension and discover how Rimu.jl represents Hamiltonians as sparse matrices while implementing projector Monte Carlo methods to sample ground and low-lying eigenstates even with huge matrix sizes. Understand the latest updates to the Rimu.jl package since JuliaCon24, including a new integration with the quantum chemistry package ElemCo.jl and innovative bias-free approaches to taming the Monte Carlo sign problem in the Fröhlich polaron model with formally and practically infinite Hilbert spaces. Gain insights into advanced computational techniques for quantum physics simulations and the practical implementation of Monte Carlo methods for eigenstate sampling in Julia.