Parameter Setting for Quantum Annealers Kristen L. Pudenz, Lockheed Martin Aeronautics We develop and apply several strategies for setting physical parameters on quantum annealers for application prob-lems that do not fit natively on the hardware graph. The strategies are tested with a culled random set of mixed satisfiability problems, yielding results that generalize to guidelines regarding which parameter setting strategies to use for different classes of problems, and how to choose other necessary hardware quantities as well. Alternate methods of changing the hardware implementation of an application problem are also considered and their utility discussed. Abstractions Considered Helpful:  A Tools Architecture for Quantum Annealers Michael Booth, Edward Dahl, Mark Furtney, Steven P. Reinhardt, D- Wave Systems, Inc. Today’s usable quantum computers, variously known as adiabatic quantum computers or quantum annealers and exemplified by the D- Wave 2X™ system, have an instruction set architecture foreign to mainstream classical computers and thus require a new class of programming tools to enable their widespread use. We submit that well-chosen abstractions, each balancing the ability of high- and low- level tools to use it, will play an essential role in fostering a vibrant ecosystem of such new tools. We propose the virtual quadratic unconstrained binary optimization (vQUBO) problem as one such abstraction and describe our experience in implementing and using it. As one step toward an effective quantum computing ecosystem, we invite other tool developers to create complementary tools that map from user problems to the vQUBO form for end-to-end usability and performance. An Approach to Big Data Inspired by Statistical Mechanics John A. Cortese, MIT Lincoln Laboratory A family of techniques in physics known as statistical mechanics is useful for describing the macroscopic properties of materials composed of a large (Avogadro’s #   1024) number of atoms. This talk applies the same approach to the analysis of big data problems. The initial problem examined is that of classification, specifically binary hypothesis testing, in the big data, high dimensional scenario. The lessons learned from the binary hypothesis testing problem are extended to other signal processing paradigms. Associative Array Model of SQL, NoSQL, and NewSQL Databases Jeremy Kepner 1 , 2 , 3 , Vijay Gadepally 1 , 2 , Dylan Hutchison 4 , Hayden Jananthan 3 , 5 , Timothy Mattson 6 , Siddharth Samsi 1 , Albert Reuther 1 1 MIT Lincoln Laboratory, 2 MIT Computer Science & AI Laboratory, 3 MIT Mathematics Department, 4 University of Washington Computer Science Department, 5 Vanderbilt University Mathematics Department, 6 Intel Corporation The success of SQL, NoSQL, and NewSQL databases   is a reflection of their ability to provide significant functionality and performance benefits for specific domains, such as financial transactions, internet search, and data analysis. The BigDAWG polystore seeks to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Associative arrays provide a common approach to the mathematics found in different databases: sets (SQL), graphs (NoSQL), and matrices (NewSQL). This work presents the SQL relational model in terms of associative arrays and identifies the key mathematical properties that are preserved within SQL. These properties include associativity, commutativity, distributivity, identities, annihilators, and inverses. Performance measurements on distributivity and associativity show the impact these properties can have on associative array operations. These results demonstrate that associative arrays could provide a mathematical model for polystores to optimize the exchange of data and execution queries.