Instruction Set Architectures for Quantum Processing Units

Progress in quantum computing hardware raises questions about how these devices can be controlled, programmed, and integrated with existing computational workflows. We briefly describe several prominent quantum computational models, their associated quantum processing units (QPUs), and the adoption of these devices as accelerators within high-performance computing systems. Emphasizing the interface to the QPU, we analyze instruction set architectures based on reduced and complex instruction sets, i.e., RISC and CISC architectures. We clarify the role of conventional constraints on memory addressing and instruction widths within the quantum computing context. Finally, we examine existing quantum computing platforms, including the D-Wave 2000Q and IBM Quantum Experience, within the context of future ISA development and HPC needs.Comment: To be published in the proceedings in the International Super Computing Conference 2017 publicatio

Properties of Healthcare Teaming Networks as a Function of Network Construction Algorithms

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other. Most healthcare service network models have been constructed from patient claims data, using billing claims to link patients with providers. The data sets can be quite large, making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks. To address this issue, we compared the properties of healthcare networks constructed using different algorithms and the 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We found that each algorithm produced networks with substantially different topological properties. Provider networks adhered to a power law, and organization networks to a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and greatly altered measures of vertex prominence such as the betweenness centrality. We identified patterns in the distance patients travel between network providers, and most strikingly between providers in the Northeast United States and Florida. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications for selecting the algorithm best suited to the type of analysis to be performed.Comment: With links to comprehensive, high resolution figures and networks via figshare.co

Complementary network-based approaches for exploring genetic structure and functional connectivity in two vulnerable, endemic ground squirrels

The persistence of small populations is influenced by genetic structure and functional connectivity. We used two network-based approaches to understand the persistence of the northern Idaho ground squirrel (Urocitellus brunneus) and the southern Idaho ground squirrel (U. endemicus), two congeners of conservation concern. These graph theoretic approaches are conventionally applied to social or transportation networks, but here are used to study population persistence and connectivity. Population graph analyses revealed that local extinction rapidly reduced connectivity for the southern species, while connectivity for the northern species could be maintained following local extinction. Results from gravity models complemented those of population graph analyses, and indicated that potential vegetation productivity and topography drove connectivity in the northern species. For the southern species, development (roads) and small-scale topography reduced connectivity, while greater potential vegetation productivity increased connectivity. Taken together, the results of the two network-based methods (population graph analyses and gravity models) suggest the need for increased conservation action for the southern species, and that management efforts have been effective at maintaining habitat quality throughout the current range of the northern species. To prevent further declines, we encourage the continuation of management efforts for the northern species, whereas conservation of the southern species requires active management and additional measures to curtail habitat fragmentation. Our combination of population graph analyses and gravity models can inform conservation strategies of other species exhibiting patchy distributions

spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R

In this paper, we present an R package that combines feature-based (X) data and graph-based (G) data for prediction of the response Y . In this particular case, Y is observed for a subset of the observations (labeled) and missing for the remainder (unlabeled). We examine an approach for fitting Y = Xò + f(G) where ò is a coefficient vector and f is a function over the vertices of the graph. The procedure is semi-supervised in nature (trained on the labeled and unlabeled sets), requiring iterative algorithms for fitting this estimate. The package provides several key functions for fitting and evaluating an estimator of this type. The package is illustrated on a text analysis data set, where the observations are text documents (papers), the response is the category of paper (either applied or theoretical statistics), the X information is the name of the journal in which the paper resides, and the graph is a co-citation network, with each vertex an observation and each edge the number of times that the two papers cite a common paper. An application involving classification of protein location using a protein interaction graph and an application involving classification on a manifold with part of the feature data converted to a graph are also presented.