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MF-LBM: A Portable, Scalable and High-performance Lattice Boltzmann Code for DNS of Flow in Porous Media

How do you encode combinatorial optimization problems when they have inequality constraints on quantum computers? well, the usual approach is using slack variables. But, this approach is quite expensive and makes the search for possible solutions even harder. In this work, https://arxiv.org/abs/2211.13914, we present “unbalanced penalization” a new approach to encode the inequality constraints of combinatorial optimization problems.

A teeny tiny demo of how to publish your code on GitLab to Zenodo, following good practices with Citation File Format and CI/CD.

⚠️ NOTE :: THIS DOES NOT WORK YET :: NOTE ⚠️

Tool for generating random exact-cover problems. It prints out the exact-cover matrix and the unique ground state.

Our team consists of researches in the field of Communication Networks and Computation Engineering, Physics, and Quantum Computing. Within in this collaboration, we aim at investigating if the new D-Wave Advantage solver in Jülich can be used as an incredibly fast quantum heuristic to reconfigure wide-area networks, in tandem with the current classical implementation.

Here you can find software and data associate with our projects.

Datasets, Transforms and Models specific to Computer Vision

Multiple-scattering simulation for quasielastic neutron spectrometers. This work is currently stalled. If you are interested to give it a try, then please contact me so that I can give you some hints.

Introduction to neutron science at MLZ

Compute self-absorption coefficients.

Postprocessing routines to evaluate the results of the analysis of emission factors within the UBA project