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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.

Datasets, Transforms and Models specific to Computer Vision

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

Deep learning stem segmentation wrapped in an Ice (ZeroC) server

Python package implementing a quantum Boltzmann machine.