Publications

Wendt, Jeremy D.; Field, Richard V.; Phillips, Cynthia A.; Prasadan, Arvind P.; Wilson, Tegan W.; Soundarajan, Sucheta S.; Bhowmick, Sanjukta B.

Abstract not provided.

Field Jr., Richard V.; Wendt, Jeremy D.; Phillips, Cynthia A.; Wilson, Tegan W.; Soundarajan, Sucheta S.; Bhowmick, Sanjukta B.

Abstract not provided.

Wendt, Jeremy D.; Kegelmeyer, William P.; Pinar, Ali P.; Shead, Timothy M.; Saavedra, Gary J.; Safta, Cosmin S.; Bertino, Joseph B.

Abstract not provided.

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

Abstract not provided.

Wendt, Jeremy D.; Field, Richard V.; Garcia, Daniel G.; Quach, Tu-Thach Q.; Wells, Randall W.; Zage, David J.; Phillips, Cynthia A.; Pinar, Ali P.; Soundarajan, Sucheta S.

Abstract not provided.

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.

Davis, Warren L.; Dixon, Kevin R.; Martin, Nathaniel M.; Wendt, Jeremy D.

People responding to high-consequence national-security situations need tools to help them make the right decision quickly. The dynamic, time-critical, and ever-changing nature of these situations, especially those involving an adversary, require models of decision support that can dynamically react as a situation unfolds and changes. Automated knowledge capture is a key part of creating individualized models of decision making in many situations because it has been demonstrated as a very robust way to populate computational models of cognition. However, existing automated knowledge capture techniques only populate a knowledge model with data prior to its use, after which the knowledge model is static and unchanging. In contrast, humans, including our national-security adversaries, continually learn, adapt, and create new knowledge as they make decisions and witness their effect. This artificial dichotomy between creation and use exists because the majority of automated knowledge capture techniques are based on traditional batch machine-learning and statistical algorithms. These algorithms are primarily designed to optimize the accuracy of their predictions and only secondarily, if at all, concerned with issues such as speed, memory use, or ability to be incrementally updated. Thus, when new data arrives, batch algorithms used for automated knowledge capture currently require significant recomputation, frequently from scratch, which makes them ill suited for use in dynamic, timecritical, high-consequence decision making environments. In this work we seek to explore and expand upon the capabilities of dynamic, incremental models that can adapt to an ever-changing feature space.

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.