RBM Image Generation Using the D-Wave 2000Q
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2019-10-30
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Citation of Original Publication
Jennifer Sleeman, Milton Halem and John Dorband, RBM Image Generation Using the D-Wave 2000Q, Presented at the EECS Rising Star Workshop, https://ebiquity.umbc.edu/paper/html/id/882/RBM-Image-Generation-Using-the-D-Wave-2000Q
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This is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
This is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
We describe a hybrid approach that combines a deep convolutional neural network autoencoder and a quantum Restricted Boltzmann Machine (RBM) for image generation using the D-Wave 2000Q. We compare the quantum learned distribution with the classical learned distribution, and quantify the quantum effects on latent representations.