RBM Image Generation Using the D-Wave 2000Q
Files
Permanent Link
Author/Creator
Author/Creator ORCID
Date
Type of Work
Department
Program
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
Rights
Public Domain Mark 1.0
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
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.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
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.
Subjects
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.