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pyQBTNs - Python Quantum Boolean Tensor Networks

Build Status License Python Version DOI

pyQBTNs was developed as a tool python to factor boolean tensors (or matrices) using D-Wave quantum annealers. The methodologies for pyQBTNs are described in [1] and [2].

pyQBTNs includes five different boolean tensor factorization methods, making up three distinct types of tensor networks.
pyQBTNs allows the user to specify local solvers that do not require a connection to a quantum annealer, but still solve the optimization problems the annealer would solve in the factorization algorithm.
In order to use a D-Wave quantum annealer as the solver for this software, the user must set up a D-Wave configuration file.

Installation

Option 1: Install using pip

pip install git+https://github.com/lanl/pyQBTNs

Option 2: Install from source

git clone https://github.com/lanl/pyQBTNs
cd pyQBTNs
conda create --name pyQBTNs python=3.7.3
source activate pyQBTNs
python setup.py install

Setup and Verify D-Wave connection

  1. Install pyQBTNs.
  2. Sign up with D-Wave Leap.
    • Make sure that you have at least 1 minute of QPU time on your free acccount.
  3. Set up D-Wave config file.
    • You can use either an Advantage system or a 2000Q system, but NOT a Hybrid solver
  4. Run an example:
cd tests
python -m unittest TestMatrixFactorizationQuantum.py

Note: For more detailed description of the D-Wave setup process see the tutorials or the example notebook on D-Wave.

Example Usage

import numpy as np
from pyQBTNs import QBTNs

qbtns = QBTNs(factorization_method="Matrix_Factorization")

X = np.random.choice(a=[False, True], size=(10, 10))

qbtns.fit(X, 2)

score = qbtns.get_score()
print(score)

Prerequisites

  • Anaconda(Optional)
  • pyparsing==2.4.7
  • decorator==4.3.0
  • dwave-ocean-sdk>=3.3.0
  • numpy>=1.19.4
  • tensorly>=0.4.5
  • sympy>=1.7.1
  • networkx>=2.5
  • nimfa>=1.4.0
  • scikit-learn>=0.24.1
  • matplotlib>=3.4.2
  • Pillow>=8.2.0

How to Cite pyQBTNs?

@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}
}

@misc{pelofske2021quantum,
      title={Quantum Annealing Algorithms for Boolean Tensor Networks},
      author={Elijah Pelofske and Georg Hahn and Daniel O'Malley and Hristo N. Djidjev and Boian S. Alexandrov},
      year={2021},
      eprint={2107.13659},
      archivePrefix={arXiv},
      primaryClass={quant-ph}
}

Authors

  • Elijah Pelofske: Information Sciences, Los Alamos National Laboratory
  • Hristo Djidjev: Information Sciences, Los Alamos National Laboratory
  • Dan O'Malley: Computational Earth Science, Los Alamos National Laboratory
  • Maksim E. Eren: Advanced Research in Cyber Systems, Los Alamos National Laboratory
  • Boian S. Alexandrov: Theoretical Division, Los Alamos National Laboratory

Copyright Notice:

© 2021. Triad National Security, LLC. All rights reserved.
This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
Department of Energy/National Nuclear Security Administration. All rights in the program are
reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear
Security Administration. The Government is granted for itself and others acting on its behalf a
nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare
derivative works, distribute copies to the public, perform publicly and display publicly, and to permit
others to do so.

LANL C Number: C21027

License:

This program is open source under the BSD-3 License.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and
    the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
    and the following disclaimer in the documentation and/or other materials provided with the
    distribution.

  3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse
    or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

References

[1] Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H. N., & Alexandrov, B. S. (2021). Boolean Hierarchical Tucker Networks on Quantum Annealers. arXiv preprint arXiv:2103.07399.

[2] Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H. N., & Alexandrov, B. S. (2021). Quantum Annealing Algorithms for Boolean Tensor Networks. arXiv preprint arXiv:2107.13659.

[3] D. O’Malley, H. N. Djidjev and B. S. Alexandrov, "Tucker-1 Boolean Tensor Factorization with Quantum Annealers," 2020 International Conference on Rebooting Computing (ICRC), 2020, pp. 58-65, doi: 10.1109/ICRC2020.2020.00002.

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