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}}
}
@article{pelofske2021quantum,
title={Quantum Annealing Algorithms for Boolean Tensor Networks},
author={Pelofske, Elijah and Hahn, Georg and O'Malley, Daniel and Djidjev, Hristo N and Alexandrov, Boian S},
journal={arXiv preprint arXiv:2107.13659},
year={2021}
}
@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}
}