  Biclustering is a problem in machine learning and data mining that seeks to
group together rows and columns of a dataset according to certain criteria. In
this work, we highlight the natural relation that quantum computing models like
boson and Gaussian boson sampling (GBS) have to this problem. We first explore
the use of boson sampling to identify biclusters based on matrix permanents. We
then propose a heuristic that finds clusters in a dataset using Gaussian boson
sampling by (i) converting the dataset into a bipartite graph and then (ii)
running GBS to find the densest sub-graph(s) within the larger bipartite graph.
Our simulations for the above proposed heuristics show promising results for
future exploration in this area.
