Publications

Maunz, Peter L.; Blume-Kohout, Robin J.; Blain, Matthew G.; Benito, Francisco B.; Berry, Christopher W.; Clark, Craig R.; Clark, Susan M.; Colombo, Anthony P.; Dagel, Amber L.; Fortier, Kevin M.; Haltli, Raymond A.; Heller, Edwin J.; Lobser, Daniel L.; Mizrahi, Jonathan M.; Nielsen, Erik N.; Resnick, Paul J.; Rembetski, John F.; Rudinger, Kenneth M.; Scrymgeour, David S.; Sterk, Jonathan D.; Tabakov, Boyan T.; Tigges, Chris P.; Van Der Wall, Jay W.; Stick, Daniel L.

Abstract not provided.

Parekh, Ojas D.; Aidun, John B.; Dubicka, Irene D.; Landahl, Andrew J.; Shulenburger, Luke N.; Tigges, Chris P.; Wendt, Jeremy D.

This report summarizes the first year’s effort on the Enceladus project, under which Sandia was asked to evaluate the potential advantages of adiabatic quantum computing for analyzing large data sets in the near future, 5-to-10 years from now. We were not specifically evaluating the machine being sold by D-Wave Systems, Inc; we were asked to anticipate what future adiabatic quantum computers might be able to achieve. While realizing that the greatest potential anticipated from quantum computation is still far into the future, a special purpose quantum computing capability, Adiabatic Quantum Optimization (AQO), is under active development and is maturing relatively rapidly; indeed, D-Wave Systems Inc. already offers an AQO device based on superconducting flux qubits. The AQO architecture solves a particular class of problem, namely unconstrained quadratic Boolean optimization. Problems in this class include many interesting and important instances. Because of this, further investigation is warranted into the range of applicability of this class of problem for addressing challenges of analyzing big data sets and the effectiveness of AQO devices to perform specific analyses on big data. Further, it is of interest to also consider the potential effectiveness of anticipated special purpose adiabatic quantum computers (AQCs), in general, for accelerating the analysis of big data sets. The objective of the present investigation is an evaluation of the potential of AQC to benefit analysis of big data problems in the next five to ten years, with our main focus being on AQO because of its relative maturity. We are not specifically assessing the efficacy of the D-Wave computing systems, though we do hope to perform some experimental calculations on that device in the sequel to this project, at least to provide some data to compare with our theoretical estimates.

Sterk, Jonathan D.; Blume-Kohout, Robin J.; Parekh, Ojas D.; Maunz, Peter L.; Tigges, Chris P.; Blain, Matthew G.; Colbaugh, Richard C.; Johnson, Cort N.

Abstract not provided.