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Source code and data corresponding to the publication 'Reliability and subject specificity of personalized whole-brain dynamical models'

Source code corresponding to the manuscript "Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels"

Data base API associated numeric partial wave decompositions

Code related to the CHRRT (Coordinate Hit-and-Run with Rounding and Thinning) project.

Contains scripts for sampling & evaluating performance. Also contains ESS and RHAT data for the runs used in the manuscript.

Jupyter notebook for generating instances of the garden optimization QUBO problem.

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.

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.

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