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  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=restricted+boltzmann+machine]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for restricted boltzmann machine]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/882/RBM-Image-Generation-Using-the-D-Wave-2000Q"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/883/RBM-Image-Generation-Using-the-D-Wave-2000Q"/>
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 <item 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">
  <title><![CDATA[A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning</link>
  <description><![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...]]></description>
  <dc:date>2020-05-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning">
  <title><![CDATA[A Hybrid Quantum Enabled RBM Advantage: Convolutional Autoencoders for Quantum Image Compression and Generative Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning</link>
  <description><![CDATA[Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our hybrid autoencoder algorithm using two datasets, the MNIST dataset and MNIST Fashion dataset. We evaluate the qua...]]></description>
  <dc:date>2020-01-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/882/RBM-Image-Generation-Using-the-D-Wave-2000Q">
  <title><![CDATA[RBM Image Generation Using the D-Wave 2000Q]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/882/RBM-Image-Generation-Using-the-D-Wave-2000Q</link>
  <description><![CDATA[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.]]></description>
  <dc:date>2019-10-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/883/RBM-Image-Generation-Using-the-D-Wave-2000Q">
  <title><![CDATA[RBM Image Generation Using the D-Wave 2000Q]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/883/RBM-Image-Generation-Using-the-D-Wave-2000Q</link>
  <description><![CDATA[Currently implementing machine learning using the D-Wave system for image processing is challenged by the limited number of
qubits available. A commonly used neural network method on the D-Wave is the Restricted Boltzmann Machine (RBM) as it uses
an energy-based model that is consistent with the D-Wave, whereby the D-Wave acts as a physical Boltzmann machine.
However, there has been limited practical application of RBMs for use in image generation due to the limited number of qubits
and d...]]></description>
  <dc:date>2019-09-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing">
  <title><![CDATA[Variational Autoencoders using D-Wave Quantum Annealing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing</link>
  <description><![CDATA[Exploring the use of deep learning algorithms on the quantum computer will provide insight into how the quantum computer, in particular quantum annealing, can be applied to climate related research to accelerate the learning process. Current research has explored using Restricted Boltzmann Machines (RBM) using D-Wave's quantum annealer. This work has explored problems such as MNIST image recognition tasks. In addition, another body of research has explored variational inference methods using ...]]></description>
  <dc:date>2018-12-10</dc:date>
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