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ASCR Workshop on Quantum Computing for Science

Aspuru-Guzik, Alan A.; van Dam, Wim v.; Farhi, Edward F.; Gaitan, Frank G.; Humble, Travis S.; Jordan, Stephen J.; Landahl, Andrew J.; Love, Peter J.; Lucas, Robert F.; Preskill, John P.; Muller, Richard P.; Svore, Krysta S.; Wiebe, Nathan W.; Williams, Carl W.

This report details the findings of the DOE ASCR Workshop on Quantum Computing for Science that was organized to assess the viability of quantum computing technologies to meet the computational requirements of the DOE’s science and energy mission, and to identify the potential impact of quantum technologies. The workshop was held on February 17-18, 2015, in Bethesda, MD, to solicit input from members of the quantum computing community. The workshop considered models of quantum computation and programming environments, physical science applications relevant to DOE's science mission as well as quantum simulation, and applied mathematics topics including potential quantum algorithms for linear algebra, graph theory, and machine learning. This report summarizes these perspectives into an outlook on the opportunities for quantum computing to impact problems relevant to the DOE’s mission as well as the additional research required to bring quantum computing to the point where it can have such impact.

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TYPE Other Report YEAR 2015
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Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis

Parekh, Ojas D.; Wendt, Jeremy D.; Shulenburger, Luke N.; Landahl, Andrew J.; Moussa, Jonathan E.; Aidun, John B.

We lay the foundation for a benchmarking methodology for assessing current and future quantum computers. We pose and begin addressing fundamental questions about how to fairly compare computational devices at vastly different stages of technological maturity. We critically evaluate and offer our own contributions to current quantum benchmarking efforts, in particular those involving adiabatic quantum computation and the Adiabatic Quantum Optimizers produced by D-Wave Systems, Inc. We find that the performance of D-Wave's Adiabatic Quantum Optimizers scales roughly on par with classical approaches for some hard combinatorial optimization problems; however, architectural limitations of D-Wave devices present a significant hurdle in evaluating real-world applications. In addition to identifying and isolating such limitations, we develop algorithmic tools for circumventing these limitations on future D-Wave devices, assuming they continue to grow and mature at an exponential rate for the next several years.

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TYPE SAND Report YEAR 2015
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Results 1–25 of 72
Results 1–25 of 72