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Quantum Graph Analysis

Maunz, Peter L.W.; Sterk, Jonathan D.; Lobser, Daniel; Parekh, Ojas D.; Ryan-Anderson, Ciaran

In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

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TYPE SAND Report YEAR 2015
DOIOSTI

Micro-fabricated ion traps for Quantum Information Processing; Highlights and lessons learned

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

Abstract not provided.

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TYPE Conference Poster YEAR 2015
OSTI

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Kim, Kyungjoo; Rajamanickam, Sivasankaran; Stelle, George W.; Edwards, Harold C.; Olivier, Stephen L.

We introduce a task-parallel algorithm for sparse incomplete Cholesky factorization that utilizes a 2D sparse partitioned-block layout of a matrix. Our factorization algorithm follows the idea of algorithms-by-blocks by using the block layout. The algorithm-byblocks approach induces a task graph for the factorization. These tasks are inter-related to each other through their data dependences in the factorization algorithm. To process the tasks on various manycore architectures in a portable manner, we also present a portable tasking API that incorporates different tasking backends and device-specific features using an open-source framework for manycore platforms i.e., Kokkos. A performance evaluation is presented on both Intel Sandybridge and Xeon Phi platforms for matrices from the University of Florida sparse matrix collection to illustrate merits of the proposed task-based factorization. Experimental results demonstrate that our task-parallel implementation delivers about 26.6x speedup (geometric mean) over single-threaded incomplete Choleskyby- blocks and 19.2x speedup over serial Cholesky performance which does not carry tasking overhead using 56 threads on the Intel Xeon Phi processor for sparse matrices arising from various application problems.

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TYPE Other Report YEAR 2015
DOIOSTI
Results 5051–5075 of 9,998
Results 5051–5075 of 9,998