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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/136/Making-Google-Richer-Optimal-Algorithms-for-the-AdWords-Auction"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/136/Making-Google-Richer-Optimal-Algorithms-for-the-AdWords-Auction">
  <title><![CDATA[Making Google Richer: Optimal Algorithms for the AdWords Auction]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/136/Making-Google-Richer-Optimal-Algorithms-for-the-AdWords-Auction</link>
  <description><![CDATA[Google's phenomenal success may be ascribed as much to a revolution in advertising as to its superior search results. The key to this online advertising model is an innovative auction run by the search engine companies. Advertisers' bids for search keywords determine which ads are displayed during internet searches. There are a number of algorithmic and game theoretic issues that arise in this context. In particular, the problem of maximizing revenue in such keyword auctions can be formulated...]]></description>
  <dc:date>2006-03-03</dc:date>
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 <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>
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 <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>
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  <title><![CDATA[Quantum-Assisted Greedy Algorithms]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/876/Quantum-Assisted-Greedy-Algorithms</link>
  <description><![CDATA[We show how to leverage quantum annealers 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 quantum annealers that sample from the ground state of Ising Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables. More specifically, we look at each spin in the Ising model as a random variable a...]]></description>
  <dc:date>2019-12-05</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/845/Quantum-Annealing-Based-Binary-Compressive-Sensing-with-Matrix-Uncertainty">
  <title><![CDATA[Quantum Annealing Based Binary Compressive Sensing  with Matrix Uncertainty]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/845/Quantum-Annealing-Based-Binary-Compressive-Sensing-with-Matrix-Uncertainty</link>
  <description><![CDATA[Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional signal processing techniques in acquiring and reconstructing such signals. Compressive sensing with matrix uncertainty is an extension of the standard compressive sensing problem that appears in various applications, including but not limited to cognitive radio sensing, calibration of the antenna, and deconvolution. The...]]></description>
  <dc:date>2019-01-01</dc:date>
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