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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=quantum">
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/367/What-is-Quantum-Computing-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/219/Scalable-Solver-Infrastructure-for-Computational-Science-Engineering"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/20/Applying-AI-And-Multi-Agent-Techniques-To-National-Security-And-Business-Operations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1049/Quantum-A-New-Kind-of-Knowledge-Discovery"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1019/Quantum-Assisted-Greedy-Algorithms"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/986/Multi-Qubit-Correction-for-Quantum-Annealers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/952/Post-Quantum-Error-Correction-for-Quantum-Annealers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/907/An-Ensemble-Approach-for-Compressive-Sensing-with-Quantum-Annealers"/>
      <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/897/Reinforcement-Quantum-Annealing-A-Hybrid-Quantum-Learning-Automata"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers"/>
      <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/880/Reinforcement-Quantum-Annealing-A-Quantum-Assisted-Learning-Automata-Approach"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/367/What-is-Quantum-Computing-">
  <title><![CDATA[What is Quantum Computing?]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/367/What-is-Quantum-Computing-</link>
  <description><![CDATA[This talk will give an introductory overview of quantum computing in an intuitive and conceptual fashion. No prior knowledge of quantum mechanics will be assumed.]]></description>
  <dc:date>2010-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/219/Scalable-Solver-Infrastructure-for-Computational-Science-Engineering">
  <title><![CDATA[Scalable Solver Infrastructure for Computational Science & Engineering]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/219/Scalable-Solver-Infrastructure-for-Computational-Science-Engineering</link>
  <description><![CDATA[Multiscale, multirate scientific and engineering applications based on systems of partial differential equations possess resolution requirements that demand execution on the highest-capability computers available, which will soon reach the petascale. While the variety of applications is enormous, their needs for mathematical software infrastructure are surprisingly coincident. Implicit methods for transient and equilibrium problems lead after discretization to large, ill-conditioned algebraic...]]></description>
  <dc:date>2007-10-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/20/Applying-AI-And-Multi-Agent-Techniques-To-National-Security-And-Business-Operations">
  <title><![CDATA[Applying AI And Multi-Agent Techniques To National Security And Business Operations]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/20/Applying-AI-And-Multi-Agent-Techniques-To-National-Security-And-Business-Operations</link>
  <description><![CDATA[In Today'S Economic Environment There Is An Urgent Need For
Both Commercial And Government Enterprises To React
Quickly, Flexibly, And Accurately To Changing
Conditions. Under Contract To The Us Government, Quantum
Leap Innovations (Qli) Is Developing And Applying
Artificial Intelligence And Multi-Agent System Techniques
To Help Provide Early Detection And Response To National
Security Threats, Such As Biological And Chemical
Attacks. The Three-Phase Approach Incorporates Awareness
(...]]></description>
  <dc:date>2004-04-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1049/Quantum-A-New-Kind-of-Knowledge-Discovery">
  <title><![CDATA[Quantum: A New Kind of Knowledge Discovery]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1049/Quantum-A-New-Kind-of-Knowledge-Discovery</link>
  <description><![CDATA[While the first solid-state device (known as transistor) was being developed at Bell lab in the mid-twentieth
to replace vacuum-tubes, artificial intelligence (AI) was being conceptualized by a generation
of scientists, mathematicians, and philosophers. In 1950, Alan Turing suggested two criteria
for machine intelligence: memory for enabling machines to store and retrieve data, and reasoning (i.e.,
having the capacity to process data). Since then, trends in doubling the transistor count, ...]]></description>
  <dc:date>2022-11-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1019/Quantum-Assisted-Greedy-Algorithms">
  <title><![CDATA[Quantum-Assisted Greedy Algorithms]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1019/Quantum-Assisted-Greedy-Algorithms</link>
  <description><![CDATA[We show how to leverage quantum annealers (QAs) to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making locally optimal choices at each stage, we use QAs that sample from the ground state of problem-dependent Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables. More specifically, we look at each spin of the Ising model as a random variab...]]></description>
  <dc:date>2022-07-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/986/Multi-Qubit-Correction-for-Quantum-Annealers">
  <title><![CDATA[Multi-Qubit Correction for Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/986/Multi-Qubit-Correction-for-Quantum-Annealers</link>
  <description><![CDATA[We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with a lower energy value.  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels — i.e., a group of qubits that changing their states simultaneously can result in a new state with a lower energy ...]]></description>
  <dc:date>2021-07-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/952/Post-Quantum-Error-Correction-for-Quantum-Annealers">
  <title><![CDATA[Post-Quantum Error-Correction for Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/952/Post-Quantum-Error-Correction-for-Quantum-Annealers</link>
  <description><![CDATA[We present a general post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC), that views the evolution in an open-system as a Gibbs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels—i.e., a group of qubits that changing their states simultaneously can result in a n...]]></description>
  <dc:date>2020-09-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/907/An-Ensemble-Approach-for-Compressive-Sensing-with-Quantum-Annealers">
  <title><![CDATA[An Ensemble Approach for Compressive Sensing with Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/907/An-Ensemble-Approach-for-Compressive-Sensing-with-Quantum-Annealers</link>
  <description><![CDATA[We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing.  Since executing quantum machine instructions on a quantum annealer can result in an excited state, rather than the ground state of the given Hamiltonian, we use different penalty parameters to generate multiple distinct quadratic unconstrained binary optimization (QUBO) functions whose ground state(s) represent a potential solution of the original problem.  We then emp...]]></description>
  <dc:date>2020-06-19</dc:date>
 </item>
 <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/897/Reinforcement-Quantum-Annealing-A-Hybrid-Quantum-Learning-Automata">
  <title><![CDATA[Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/897/Reinforcement-Quantum-Annealing-A-Hybrid-Quantum-Learning-Automata</link>
  <description><![CDATA[We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an intelligent
agent searches in the space of Hamiltonians and interacts with a quantum annealer that plays the
stochastic environment role of learning automata. At each iteration of RQA, after analyzing results
(samples) from the previous iteration, the agent adjusts the penalty of unsatisfied constraints and
re-casts the given problem to a new Ising Hamiltonian. As a proof-of-concept, we propose a novel
ap...]]></description>
  <dc:date>2020-05-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers">
  <title><![CDATA[Leveraging Artificial Intelligence to Advance Problem-Solving with Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers</link>
  <description><![CDATA[We show how to advance quantum information processing, specifically problem-solving with quantum annealers, in the realm of artificial intelligence.  We introduce SAT++, as a novel quantum programming paradigm, that can compile classical algorithms (implemented in classical programming languages) and execute them on quantum annealers.   Moreover, we introduce a post-quantum error correction method that can find samples with significantly lower energy values, compared to the state-of-the-art t...]]></description>
  <dc:date>2020-05-01</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/880/Reinforcement-Quantum-Annealing-A-Quantum-Assisted-Learning-Automata-Approach">
  <title><![CDATA[Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/880/Reinforcement-Quantum-Annealing-A-Quantum-Assisted-Learning-Automata-Approach</link>
  <description><![CDATA[Superseded by.:  Ramin Ayanzadeh, Milton Halem, and Tim Finin, Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata, Nature Scientific Reports, v10, n1, May 2020.




We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we pro...]]></description>
  <dc:date>2020-01-01</dc:date>
 </item>
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