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	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning">
		<rdfs:label><![CDATA[A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning]]></rdfs:label>
		<pub:title><![CDATA[A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2020-05-20T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number of qubits available to accommodate typical problem sizes for fully connected quantum objective functions, and samples that are binary pixel representations. As a consequence of these limitations, we are able to show how we achieved nearly a 22-fold compression factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a lossy recovery of the original 28 x 28 grayscale images. We further show how generating samples from the D-Wave after training the RBM, resulted in 28 x 28 images that were variations of the original input data distribution, as opposed to recreating the training samples. We evaluated the quality of this method by using a downstream classification method. We formulated a MNIST classification problem using a deep convolutional neural network that used samples from the quantum RBM to train the MNIST classifier and compared the results with a MNIST classifier trained with the original MNIST training data set, as well as a MNIST classifier trained using classical RBM samples. We also explored using a secondary dataset, the MNIST Fashion dataset and demonstrate the first quantum-generated fashion. Our hybrid autoencoder approach indicates advantage for RBM results relative to the use of a current RBM classical computer implementation for image-based machine learning and even more promising results for the next generation D-Wave quantum system. Our method for compression and image mappings is not constrained to RBMs, the autoencoder part of this method could be coupled with other quantum-based algorithms.]]></pub:abstract>
		<pub:note><![CDATA[<hr/>
<a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11391/113910B/A-Hybrid-Quantum-Enabled-RBM-Advantage--Convolutional-Autoencoders-For/10.1117/12.2558832.short">Publisher's site</a>
<hr/>
<img src="https://ebiquity.umbc.edu/blogger/wp-content/uploads/2020/06/Screen-Shot-2020-06-27-at-5.32.34-PM.png" style="width:100%">
<hr/>]]></pub:note>
		<pub:volume><![CDATA[11391]]></pub:volume>
		<pub:counter>679</pub:counter>
		<pub:tag><![CDATA[autoencoder]]></pub:tag>
		<pub:tag><![CDATA[d-wave]]></pub:tag>
		<pub:tag><![CDATA[data compression]]></pub:tag>
		<pub:tag><![CDATA[deep learning]]></pub:tag>
		<pub:tag><![CDATA[quantum annealing]]></pub:tag>
		<pub:tag><![CDATA[quantum computing]]></pub:tag>
		<pub:tag><![CDATA[restricted boltzmann machine]]></pub:tag>
		<pub:booktitle><![CDATA[Quantum Information Science, Sensing, and Computation XII]]></pub:booktitle>
		<pub:publisher><![CDATA[International Society for Optics and Photonics]]></pub:publisher>
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					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/John/Dorband"><person:name><![CDATA[John E Dorband]]></person:name><rdfs:label><![CDATA[John E Dorband]]></rdfs:label></person:Person>
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							<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Jennifer/Sleeman"><person:name><![CDATA[Jennifer Sleeman]]></person:name><rdfs:label><![CDATA[Jennifer Sleeman]]></rdfs:label></person:Person>
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									<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Milton/Halem"><person:name><![CDATA[Milton Halem]]></person:name><rdfs:label><![CDATA[Milton Halem]]></rdfs:label></person:Person>
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<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/John/Dorband"><person:name><![CDATA[John E Dorband]]></person:name><rdfs:label><![CDATA[John E Dorband]]></rdfs:label></person:Person>
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