# lanl/pyQBTNs

pyQBTNs is a Python library for boolean matrix and tensor factorization using D-Wave quantum annealers. The library includes five different boolean tensor decomposition methods making up three distinct types of tensor networks. pyQBTNs is developed as part of the R&D 100 award wining SmartTensors project.

### BibTeX:

@MISC{Pelofske2021_pyQBTNs,
author = {E. {Pelofske} and H. {Djidjev} and D. {O'Malley} and M. E. {Eren} and G. {Hahn} and B. S. {Alexandrov}},
title = {pyQBTNs},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4876527},
howpublished = {\url{https://github.com/lanl/pyQBTNs}}
}

@misc{pelofske2021boolean,
title={Boolean Hierarchical Tucker Networks on Quantum Annealers},
author={Elijah Pelofske and Georg Hahn and Daniel O'Malley and Hristo N. Djidjev and Boian S. Alexandrov},
year={2021},
eprint={2103.07399},
archivePrefix={arXiv},
primaryClass={quant-ph}
}

##### Maksim E. Eren
###### Computer Science Student

My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.