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.


    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{}}

  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},

    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},
Maksim E. Eren
Maksim E. Eren
Graduate Research Assistant

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