This tutorial provides a hands-on introduction to quantum computing. It will feature the three pillars, architectures, programming, and algorithms/applications of quantum computing. Its focus is on the applicability of problems to quantum computing from a practical point, with only the necessary foundational coverage of the physics and theoretical aspects to understand quantum computing. Simulation software will be utilized complemented by access to actual quantum computers to prototype problem solutions. This should develop a better understanding of how problems are transformed into quantum algorithms and what programming language support is best suited for a given application area. As a first of its kind, to the best of our knowledge, the tutorial includes hands-on programming experience with IBM Q and D-Wave hardware.

Recently there has been a surge of interest in several prototype software systems that can embed a cloud computing image with user applications into a supercomputer’s hardware architecture. This position paper will summarize these efforts and comment on the advantages of each design and will also discuss some of the challenges that one faces with such software systems. This paper takes the position that specific types of user applications may favor one type of design over another. Different designs may have potential advantages for specific user applications and each design also brings a considerable cost to assure operability and overall computer security. A “one size fits all design” for a cost effective and portable solution for Supercomputer/cloud delivery is far from being a solved problem. Additional research and development should continue exploring various design approaches. In the end several different types of supercomputer/cloud implementations may be needed to optimally satisfy the complexity and diversity of user needs, requirements and security concerns. The authors also recommend that the community recognize a distinction when discussing cluster-type HPC/Cloud versus Supercomputer/Cloud implementations because of the substantive differences between these systems.

Conventional cloud computing architectures may seriously constrain computational throughput for high performance computing (HPC) and high-performance data (HPD) applications. The traditional approach to circumvent such problems has been to map these applications and problems onto other specialized hardware and coprocessor architectures. This is both time and resource expensive, and poses a challenge for rapidly rising demands for computation and data analytics. In this paper we report on progress to develop an alternative experimental software defined cloud implementation that virtualizes the topology of a standard HPC computational architecture. This software defined system re-arranges access to the nodes and dynamically customizes the features of the HPC hardware architecture so that they map to the specifics of the computation and data analysis application. This allows a cloud computing implementation to utilize the specialized infrastructure capabilities of an HPC system. We have created this type of user reconfigurable architecture on an IBM Blue Gene/P supercomputing environment at the Department of Energy's Argonne Leadership Computing Facility (ALCF). This pilot configuration was implemented using both an open source cloud technology called VCL (Virtual Computing Laboratory) in combination with a provisioning module called Kittyhawk. Cloud security is addressed by configuring and running a root-less version of the VCL cloud system on the ALCF's Blue Gene/P login node.