期刊
ENTROPY
卷 22, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/e22111202
关键词
quantum annealing; Boltzmann machines; machine learning; entropy; algorithms
资金
- U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research [DE-AC05-00OR22725]
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.
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