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1. CyberMAGICS: Cyber Training on Materials Genome Innovation for Computational Software for Future Engineers

   Nomura, Ken-ichi; Dev, Pratibha; Nakano, Aiichiro; Vashishta, Priya; Wei, Tao (January 2023, American Society for Engineering Education Conference)

   Computing landscape is evolving rapidly. Exascale computers have arrived, which can perform 10^18 mathematical operations per second. At the same time, quantum supremacy has been demonstrated, where quantum computers have outperformed these fastest supercomputers for certain problems. Meanwhile, artificial intelligence (AI) is transforming every aspect of science and engineering. A highly anticipated application of the emerging nexus of exascale computing, quantum computing and AI is computational design of new materials with desired functionalities, which has been the elusive goal of the federal materials genome initiative. The rapid change in computing landscape resulting from these developments has not been matched by pedagogical developments needed to train the next generation of materials engineering cyberworkforce. This gap in curricula across colleges and universities offers a unique opportunity to create educational tools, enabling a decentralized training of cyberworkforce. To achieve this, we have developed training modules for a new generation of quantum materials simulator, named AIQ-XMaS (AI and quantum-computing enabled exascale materials simulator), which integrates exascalable quantum, reactive and neural-network molecular dynamics simulations with unique AI and quantum-computing capabilities to study a wide range of materials and devices of high societal impact such as optoelectronics and health. As a singleentry access point to these training modules, we have also built a CyberMAGICS (cyber training on materials genome innovation for computational software) portal, which includes step-by-step instructions in Jupyter notebooks and associated tutorials, while providing online cloud service for those who do not have access to adequate computing platform. The modules are incorporated into our open-source AIQ-XMaS software suite as tutorial examples and are piloted in classroom and workshop settings to directly train many users at the University of Southern California (USC) and Howard University—one of the largest historically black colleges and universities (HBCUs), with a strong focus on underrepresented groups. In this paper, we summarize these educational developments, including findings from the first CyberMAGICS Workshop for Underrepresented Groups, along with an introduction to the AIQ-XMaS software suite. Our training modules also include a new generation of open programming languages for exascale computing (e.g., OpenMP target) and quantum computing (e.g., Qiskit) used in our scalable simulation and AI engines that underlie AIQ-XMaS. Our training modules essentially support unique dual-degree opportunities at USC in the emerging exa-quantum-AI era: Ph.D. in science or engineering, concurrently with MS in computer science specialized in high-performance computing and simulations, MS in quantum information science or MS in materials engineering with machine learning. The developed modular cyber-training pedagogy is applicable to broad engineering education at large. 

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2. Consideration of the Need for Quantum Grid Computing

   Rosch-Grace, Dominic Straub (July 2021, Proceedings of the 17th International Conference on Grid, Cloud, and Cluster Computing)

   null (Ed.)

   Quantum computing is poised to dramatically change the computational landscape, worldwide. Quantum computers can solve complex problems that are, at least in some cases, beyond the ability of even advanced future classical-style computers. In addition to being able to solve these classical computer-unsolvable problems, quantum computers have demonstrated a capability to solve some problems (such as prime factoring) much more efficiently than classical computing. This will create problems for encryption techniques, which depend on the difficulty of factoring for their security. Security, scientific, and other applications will require access to quantum computing resources to access their unique capabilities, speed and economic (aggregate computing time cost) benefits. Many scientific applications, as well as numerous other ones, use grid computing to provide benefits such as scalability and resource access. As these applications may benefit from quantum capabilities - and some future applications may require quantum capabilities - identifying how to integrate quantum computing systems into grid computing environments is critical. This paper discusses the benefits of grid-connected quantum computers and what is required to achieve this. 

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3. Materials challenges and opportunities for quantum computing hardware

   Nathalie P. de Leon, Kohei M. (April 2021, Science)

   Quantum computing hardware technologies have advanced during the past two decades, with the goal of building systems that can solve problems that are intractable on classical computers. The ability to realize large-scale systems depends on major advances in materials science, materials engineering, and new fabrication techniques. We identify key materials challenges that currently limit progress in five quantum computing hardware platforms, propose how to tackle these problems, and discuss some new areas for exploration. Addressing these materials challenges will require scientists and engineers to work together to create new, interdisciplinary approaches beyond the current boundaries of the quantum computing field. 

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4. Materials challenges and opportunities for quantum computing hardware

   https://doi.org/10.1126/science.abb2823  

   de Leon, Nathalie P.; Itoh, Kohei M.; Kim, Dohun; Mehta, Karan K.; Northup, Tracy E.; Paik, Hanhee; Palmer, B. S.; Samarth, N.; Sangtawesin, Sorawis; Steuerman, D. W. (April 2021, Science)

   Quantum computing hardware technologies have advanced during the past two decades, with the goal of building systems that can solve problems that are intractable on classical computers. The ability to realize large-scale systems depends on major advances in materials science, materials engineering, and new fabrication techniques. We identify key materials challenges that currently limit progress in five quantum computing hardware platforms, propose how to tackle these problems, and discuss some new areas for exploration. Addressing these materials challenges will require scientists and engineers to work together to create new, interdisciplinary approaches beyond the current boundaries of the quantum computing field. 

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5. Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

   https://doi.org/10.1186/s40537-020-00361-2  

   Huerta, E. A.; Khan, Asad; Davis, Edward; Bushell, Colleen; Gropp, William D.; Katz, Daniel S.; Kindratenko, Volodymyr; Koric, Seid; Kramer, William T. C.; McGinty, Brendan; et al (October 2020, Journal of Big Data)

   Abstract Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry. 

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