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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

Master's thesis on using quantum Boltzmann machines in quantitative finance.

Python package implementing a quantum Boltzmann machine.