H-NESSi: The Hierarchical Non-Equilibrium Systems Simulation package

TL;DR

H-NESSi is an open-source simulation package that efficiently solves non-equilibrium Green's function equations for quantum systems using hierarchical low-rank techniques, enabling large-scale, long-time simulations.

Contribution

It introduces hierarchical low-rank compression and advanced time-stepping to reduce computational cost and memory in NEGF simulations of quantum systems.

Findings

Achieves significantly reduced computational complexity compared to traditional methods.

Supports large-scale, long-time simulations of correlated quantum materials.

Demonstrates favorable scaling in benchmark applications like superconductors and the Hubbard model.

Abstract

We present H-NESSi (The Hierarchical Non-Equilibrium Systems Simulation package), an open-source software package for solving the Kadanoff-Baym equations (KBE) of nonequilibrium Green's function (NEGF) theory using hierarchical low-rank compression techniques. The simulation of strongly correlated quantum systems out of equilibrium is severely limited by the cubic scaling in propagation time and quadratic memory growth associated with conventional two-time formulations. H-NESSi overcomes these limitations by combining high-order time-stepping schemes with hierarchical off-diagonal low-rank (HODLR) representations of the retarded and lesser Green's functions, enabling controllable accuracy at substantially reduced computational cost and memory usage. Imaginary time quantities are efficiently represented using the discrete Lehmann representation (DLR), allowing compact and accurate treatment of thermal initial states. The implementation supports multiorbital systems, adaptive singular value truncation, and both shared-memory (OpenMP) and distributed-memory (MPI) parallelization strategies suitable for large-scale lattice calculations. The workflow closely mirrors established NEGF frameworks while introducing compression transparently into the propagation procedure. Benchmark applications to driven superconductors within dynamical mean-field theory and to the two-dimensional Hubbard model demonstrate favorable scaling compared to conventional implementations, with asymptotic time complexity significantly below the cubic scaling of uncompressed approaches. H-NESSi thus enables long-time and large-system nonequilibrium simulations of correlated quantum materials which were previously computationally prohibitive.