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In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (see https://www.nsf.gov/news/special_reports/big_ideas/index.jsp). One of these ideas, "The Quantum Leap: Leading the Next Quantum Revolution," advances quantum technologies of the future: quantum computing, quantum communication, quantum simulations and quantum sensors. Recent advances in understanding and exploiting quantum mechanics are laying the foundation for generations of new discoveries that can benefit society in unforeseen ways. This "quantum revolution" requires a highly-trained workforce that can advance the envelope of what is possible, through research and development of practical solutions for quantum technologies. Academic faculty serve a vital role in the development of this workforce, by training the next generation of students while performing vital research. The disciplines of computer science (CS), information science (IS), and computer engineering (CE) are at the nexus of the interdisciplinary breakthroughs needed to design advanced quantum computing, modeling, communication and sensing technologies. NSF recognizes that there is inadequate research capacity in the CS/CE disciplines in the realm of Quantum Computing & Information Science (QCIS).

In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (see https://www.nsf.gov/news/special_reports/big_ideas/index.jsp). One of these ideas, "The Quantum Leap: Leading the Next Quantum Revolution," advances quantum technologies of the future: quantum computing, quantum communication, quantum simulations and quantum sensors. Recent advances in understanding and exploiting quantum mechanics are laying the foundation for generations of new discoveries that can benefit society in unforeseen ways. This "quantum revolution" requires a highly-trained workforce that can advance the envelope of what is possible, through research and development of practical solutions for quantum technologies. Academic faculty serve a vital role in the development of this workforce, by training the next generation of students while performing vital research.

Quantum computing is a revolutionary approach to information processing based on the quantum physics of coherent superposition and entanglement.  Advantages of quantum computing include efficient algorithms for computationally difficult tasks, efficient use of resources such as memory and energy needed for computations, and new platforms for the simulation of quantum mechanical systems that are currently intractable using conventional computers.  Applications for quantum computing, such as integer number factoring, search and optimization algorithms, and quantum simulations, will accelerate discoveries in a broad range of disciplines including physics, engineering, and computer science.

PIF provides support for physics proposals in three subareas: 1) computational physics, 2) data enabled physics, and 3) quantum information science and revolutionary computing. Computational physics emphasizes methods for high-performance computing that require significant code development, are led by physicists, and may include applied mathematicians and computer scientists. Priority will be given to proposals that, in addition to compelling scientific goals, have a computational advance or new enabling capability. Proposals should include either innovation in computing such as algorithm development and efficient use of novel architectures or provide significant improvement to community codes. Data enabled physics seeks proposals to develop tools and infrastructure that provide rapid, secure, and efficient access to physics data stores via heterogeneous or distributed computing resources and networks. Examples include development of reliable digital preservation, access, integration, and curation capabilities associated with data from Physics Division experimental facilities and the tools for data handling needed to maximize the scientific payoff. Priority will be given to proposals that serve broad communities or that bring dramatic new capabilities to a specific sub-area of physics. Quantum information and revolutionary computing supports theoretical and experimental proposals that explore applications of quantum mechanics to new computing paradigms or that foster interactions between physicists, mathematicians, and computer scientists that push the frontiers of quantum-based information, transmission, and manipulation.

CMMT supports theoretical and computational materials research in the topical areas represented in DMR's Topical Materials Research Programs (these are also variously known as Individual Investigator Award (IIA) Programs, or Core Programs, or Disciplinary Programs), which include: Condensed Matter Physics (CMP), Biomaterials (BMAT), Ceramics (CER), Electronic and Photonic Materials (EPM), Metals and Metallic Nanostructures (MMN), Polymers (POL), and Solid State and Materials Chemistry (SSMC). The CMMT program supports fundamental research that advances conceptual understanding of hard and soft materials, and materials-related phenomena; the development of associated analytical, computational, and data-centric techniques; and predictive materials-specific theory, simulation, and modeling for materials research. First-principles electronic structure, quantum many-body and field theories, statistical mechanics, classical and quantum Monte Carlo, and molecular dynamics, are among the methods used in the broad spectrum of research supported in CMMT. Research may encompass the advance of new paradigms in materials research, including emerging data-centric approaches utilizing data-analytics or machine learning.

The Chemical Theory, Models and Computational Methods program supports the discovery and development of theoretical and computational methods or models to address a range of chemical challenges, with emphasis on emerging areas of chemical research.  Proposals that focus on established theoretical or computational approaches should involve innovative additions or modifications that substantially broaden their applicability.  Areas of interest include, but are not limited to, electronic structure, quantum reaction dynamics, statistical mechanics, molecular dynamics, and simulation and modeling techniques for molecular systems and systems in condensed phases.  Areas of application span the full range of chemical systems from small molecules to mesoscopic aggregates, including single molecules, biological systems and materials in condensed phases.   Despite the diverse application areas, the goal of the program is to support the development of new theoretical and computational methodologies that have the potential of being broadly applicable to a range of challenging chemical problems. We are particularly interested in fundamental areas of chemical research that are difficult or impossible to address using current synthetic, experimental, and/or computational methodologies.  We encourage the integration of innovative software development with methodological and algorithmic development, especially computational approaches that allow efficient utilization of the high end computers of the future.

SoftwareInfrastructure for Sustained Innovation (SI2) is a long-term investment focused on realizing a portion of the Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21, http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504730) vision and catalyzing new thinking, paradigms and practices in science and engineering. CIF21 envisions a linked cyberinfrastructure architecture that integrates large-scale computing, high-speed networks, massive data archives, instruments and major facilities, observatories, experiments, and embedded sensors and actuators, across the nation and the world, and that enables research at unprecedented scales, complexity, resolution, and accuracy by integrating computation, data, and experiments in novel ways. Software is a primary modality through which CIF21 innovation and discovery will be realized. It permeates all aspects and layers of cyberinfrastructure (from application codes and frameworks, programming systems, libraries and system software, to middleware, operating systems, networking and the low-level drivers). The CIF21 software infrastructure must address the complexity of this cyberinfrastructure, accommodating: disruptive hardware trends; ever-increasing data volumes; data integrity, privacy, and confidentiality; security; complex application structures and behaviors; and emerging concerns such as fault-tolerance and energy efficiency. The programs must focus on building robust, reliable and sustainable software that will support and advance sustained scientific innovation and discovery.
 The Division of Advanced Cyberinfrastructure in the Computer & Information Science & Engineering Directorate (CISE/ACI) is partnering with Directorates and Offices across the NSF to support SI2, a long-term comprehensive program focused on realizing a sustained software infrastructure that is an integral part of CIF21.