Using high-precision quantum Monte Carlo (QMC) simulations within the framework of dynamical mean field theory (DMFT), we show that the anisotropic degenerate two-orbital Hubbard model contains two consecutive orbital-selective Mott transitions (OSMTs) even in the absence of spin-flip terms and pairhopping processes. In order to reveal the second transition we carefully analyze the low-frequenc...

We present a pedagogical discussion of the Maximum Entropy Method which is a precise and systematic way of analytically continuing Euclidean-time quantum Monte Carlo results to real frequencies. Here, Bayesian statistics are used to determine which of the infinite number of real-frequency spectra are consistent with the QMC data is most probable. Bayesian inference is also used to qualify the s...

Defining accurate and compact trial wavefunctions leading to small statistical and fixed-node errors in quantum Monte Carlo (QMC) calculations is still a challenging problem. Here, we propose to make use of selected configuration interaction (CI) expansions obtained by selecting the most important determinants through a perturbative criterion. A major advantage with respect to truncated CASSCF ...

Practitioners have long noticed that quasi-Monte Carlo methods work very well on functions that are nearly superpositions of low dimensional functions. The reason is that the low dimensional coordinate projections of QMC rules can have very good equidistribution properties at sample sizes for which the original rule itself cannot have good equidistribution. This paper explores a converse propos...

We have reformulated the quantum Monte Carlo (QMC) technique so that a large part of the calculation scales linearly with the number of atoms. The reformulation is related to a recent alternative proposal for achieving linearscaling QMC, based on maximally localized Wannier orbitals (MLWO), but has the advantage of greater simplicity. The technique we propose draws on methods recently developed...

We study the randomized worst-case error and the randomized error of scrambled quasi–Monte Carlo (QMC) quadrature as proposed by Owen. The function spaces considered in this article are the weighted Hilbert spaces generated by Haar-like wavelets and the weighted Sobolev-Hilbert spaces. Conditions are found under which multivariate integration is strongly tractable in the randomized worst-case s...

Analytic mathematical models for the static spin (${G}_{\ensuremath{-}}$) and density (${G}_{+}$) local field factors uniform electron gas (UEG) as functions of wave vector are presented. These closely fit recent quantum Monte Carlo (QMC) data satisfy exact asymptotic limits. A simple functional form ${G}_{\ensuremath{-}}$ is developed; same parametrized ${G}_{+}$ yields an improvement over pre...

We present release 2.0 of the ALPS (Algorithms and Libraries for Physics Simulations) project, an open source software project to develop libraries and application programs for the simulation of strongly correlated quantum lattice models such as quantum magnets, lattice bosons, and strongly correlated fermion systems. The code development is centered on common XML and HDF5 data formats, librari...

Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schr\"odinger equation that exact in principle, but limited by flexibility of available ansatzes practice. The recently introduced deep QMC approach, specifically two deep-neural-network PauliNet and FermiNet, allows variational to reach accuracy diffusion QMC, little understood about convergence behavior su...

We present an asynchronous Quasi-Monte Carlo (QMC) algorithm for numerical integration tailored for heterogeneous environments. QMC techniques are better suited for high dimensions than adaptive methods and have generally better convergence properties than classical Monte Carlo methods. The algorithm focuses on the asynchronous computation of randomized lattice (Korobov) rules. Whereas the indi...