# Biclustering a dataset using photonic quantum computing

arXiv: https://arxiv.org/abs/2405.18622 · HTML: https://mlsanity.com/paper/2405.18622/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 76 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 5 of 5)

- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Automatic Yara Rule Generation Using Biclustering](https://mlsanity.com/paper/2009.03779/) (2020)
- [Reinforcement Learning based Recommender System using Biclustering Technique](https://mlsanity.com/paper/1801.05532/) (2018)
- [The Classical Complexity of Boson Sampling](https://mlsanity.com/paper/1706.01260/) (2017)
- [Biclustering Algorithms Based on Metaheuristics: A Review](https://mlsanity.com/paper/2203.16241/) (2022)

## Abstract (arXiv)

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.