Dynamic Topology Reconfiguration of Boltzmann Machines on Quantum Annealers. Liu, J., Yao, K., & Spedalieri, F. Entropy, 22(11):1202, November, 2020. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute![Dynamic Topology Reconfiguration of Boltzmann Machines on Quantum Annealers [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to the quantum bits of an annealer. These policies, based on correlation and entropy metrics, dynamically reconfigure the topology of Boltzmann machines during training and improve performance.
@article{liu_dynamic_2020,
title = {Dynamic {Topology} {Reconfiguration} of {Boltzmann} {Machines} on {Quantum} {Annealers}},
volume = {22},
copyright = {http://creativecommons.org/licenses/by/3.0/},
url = {https://www.mdpi.com/1099-4300/22/11/1202},
doi = {10.3390/e22111202},
abstract = {Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to the quantum bits of an annealer. These policies, based on correlation and entropy metrics, dynamically reconfigure the topology of Boltzmann machines during training and improve performance.},
language = {en},
number = {11},
urldate = {2021-02-01},
journal = {Entropy},
author = {Liu, Jeremy and Yao, Ke-Thia and Spedalieri, Federico},
month = nov,
year = {2020},
note = {Number: 11
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {Boltzmann machines, machine learning, algorithms, entropy, quantum annealing},
pages = {1202},
file = {Full Text PDF:C\:\\Users\\ktyao\\Zotero\\storage\\IYQGTX3R\\Liu et al. - 2020 - Dynamic Topology Reconfiguration of Boltzmann Mach.pdf:application/pdf;Snapshot:C\:\\Users\\ktyao\\Zotero\\storage\\ZVDCRXNM\\1202.html:text/html},
}
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