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Future Directions in Social Science
By George Loewenstein, Kathleen Musante, and Joshua A. Tucker
The Future Directions Workshop series, sponsored by the Basic Research Office in the Office of the Under Secretary of Defense for Research and Engineering, seeks to examine emerging research areas that-in the natural and formal sciences-are most likely to transform future technology capabilities and-in the social sciences-innovatively inform about how the social world works.
The report is the product of a workshop held on April 11-12, 2019 at the Basic Research Innovation Collaboration Center in Arlington, VA on the future of problem-based interdisciplinary social science research. It is intended as a resource to the Science and Technology community, including the broader federal funding community, federal laboratories, domestic industrial base, and academia.
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Future Directions of Quantum Information Processing a Workshop on the Emerging Science and Technology of Quantum Computation, Communication, and Measurement
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- Key Concepts for Future QIS Learners Workshop Output Published Online May 13, 2020 Key Concepts for Future QIS Learners Workshop output published online May 13, 2020 Background and Overview On behalf of the Interagency Working Group on Workforce, Industry and Infrastructure, under the NSTC Subcommittee on Quantum Information Science (QIS), the National Science Foundation invited 25 researchers and educators to come together to deliberate on defining a core set of key concepts for future QIS learners that could provide a starting point for further curricular and educator development activities. The deliberative group included university and industry researchers, secondary school and college educators, and representatives from educational and professional organizations. The workshop participants focused on identifying concepts that could, with additional supporting resources, help prepare secondary school students to engage with QIS and provide possible pathways for broader public engagement. This workshop report identifies a set of nine Key Concepts. Each Concept is introduced with a concise overall statement, followed by a few important fundamentals. Connections to current and future technologies are included, providing relevance and context. The first Key Concept defines the field as a whole. Concepts 2-6 introduce ideas that are necessary for building an understanding of quantum information science and its applications. Concepts 7-9 provide short explanations of critical areas of study within QIS: quantum computing, quantum communication and quantum sensing. The Key Concepts are not intended to be an introductory guide to quantum information science, but rather provide a framework for future expansion and adaptation for students at different levels in computer science, mathematics, physics, and chemistry courses. As such, it is expected that educators and other community stakeholders may not yet have a working knowledge of content covered in the Key Concepts. [Show full text]
- Simulating Quantum Field Theory with a Quantum Computer Simulating quantum field theory with a quantum computer John Preskill Lattice 2018 28 July 2018 This talk has two parts (1) Near-term prospects for quantum computing. (2) Opportunities in quantum simulation of quantum field theory. Exascale digital computers will advance our knowledge of QCD, but some challenges will remain, especially concerning real-time evolution and properties of nuclear matter and quark-gluon plasma at nonzero temperature and chemical potential. Digital computers may never be able to address these (and other) problems; quantum computers will solve them eventually, though I’m not sure when. The physics payoff may still be far away, but today’s research can hasten the arrival of a new era in which quantum simulation fuels progress in fundamental physics. Frontiers of Physics short distance long distance complexity Higgs boson Large scale structure “More is different” Neutrino masses Cosmic microwave Many-body entanglement background Supersymmetry Phases of quantum Dark matter matter Quantum gravity Dark energy Quantum computing String theory Gravitational waves Quantum spacetime particle collision molecular chemistry entangled electrons A quantum computer can simulate efficiently any physical process that occurs in Nature. (Maybe. We don’t actually know for sure.) superconductor black hole early universe Two fundamental ideas (1) Quantum complexity Why we think quantum computing is powerful. (2) Quantum error correction Why we think quantum computing is scalable. A complete description of a typical quantum state of just 300 qubits requires more bits than the number of atoms in the visible universe. Why we think quantum computing is powerful We know examples of problems that can be solved efficiently by a quantum computer, where we believe the problems are hard for classical computers. [Show full text]
- Quantum Machine Learning: Benefits and Practical Examples Quantum Machine Learning: Benefits and Practical Examples Frank Phillipson1[0000-0003-4580-7521] 1 TNO, Anna van Buerenplein 1, 2595 DA Den Haag, The Netherlands [email protected] Abstract. A quantum computer that is useful in practice, is expected to be devel- oped in the next few years. An important application is expected to be machine learning, where benefits are expected on run time, capacity and learning effi- ciency. In this paper, these benefits are presented and for each benefit an example application is presented. A quantum hybrid Helmholtz machine use quantum sampling to improve run time, a quantum Hopfield neural network shows an im- proved capacity and a variational quantum circuit based neural network is ex- pected to deliver a higher learning efficiency. Keywords: Quantum Machine Learning, Quantum Computing, Near Future Quantum Applications. 1 Introduction Quantum computers make use of quantum-mechanical phenomena, such as superposi- tion and entanglement, to perform operations on data [1]. Where classical computers require the data to be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits, which can be in superpositions of states. These computers would theoretically be able to solve certain problems much more quickly than any classical computer that use even the best cur- rently known algorithms. Examples are integer factorization using Shor's algorithm or the simulation of quantum many-body systems. This benefit is also called ‘quantum supremacy’ [2], which only recently has been claimed for the first time [3]. There are two different quantum computing paradigms. [Show full text]
- Quantum Computing: Principles and Applications Journal of International Technology and Information Management Volume 29 Issue 2 Article 3 2020 Quantum Computing: Principles and Applications Yoshito Kanamori University of Alaska Anchorage, [email protected] Seong-Moo Yoo University of Alabama in Huntsville, [email protected] Follow this and additional works at: https://scholarworks.lib.csusb.edu/jitim Part of the Communication Technology and New Media Commons, Computer and Systems Architecture Commons, Information Security Commons, Management Information Systems Commons, Science and Technology Studies Commons, Technology and Innovation Commons, and the Theory and Algorithms Commons Recommended Citation Kanamori, Yoshito and Yoo, Seong-Moo (2020) "Quantum Computing: Principles and Applications," Journal of International Technology and Information Management: Vol. 29 : Iss. 2 , Article 3. Available at: https://scholarworks.lib.csusb.edu/jitim/vol29/iss2/3 This Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Journal of International Technology and Information Management by an authorized editor of CSUSB ScholarWorks. For more information, please contact [email protected] . Journal of International Technology and Information Management Volume 29, Number 2 2020 Quantum Computing: Principles and Applications Yoshito Kanamori (University of Alaska Anchorage) Seong-Moo Yoo (University of Alabama in Huntsville) ABSTRACT The development of quantum computers over the past few years is one of the most significant advancements in the history of quantum computing. D-Wave quantum computer has been available for more than eight years. IBM has made its quantum computer accessible via its cloud service. Also, Microsoft, Google, Intel, and NASA have been heavily investing in the development of quantum computers and their applications. [Show full text]
- Is Quantum Search Practical? Q UANTUM C OMPUTING IS QUANTUM SEARCH PRACTICAL? Gauging a quantum algorithm’s practical significance requires weighing it against the best conventional techniques applied to useful instances of the same problem. The authors show that several commonly suggested applications of Grover’s quantum search algorithm fail to offer computational improvements over the best conventional algorithms. ome researchers suggest achieving mas- polynomial-time algorithm for number factoring sive speedups in computing by exploiting is known, and the security of the RSA code used quantum-mechanical effects such as su- on the Internet relies on this problem’s difficulty. perposition (quantum parallelism) and If a large and error-tolerant quantum computer Sentanglement.1 A quantum algorithm typically were available today, running Shor’s algorithm on consists of applying quantum gates to quantum it could compromise e-commerce. states, but because the input to the algorithm Lov Grover’s quantum search algorithm is also might be normal classical bits (or nonquantum), widely studied. It must compete with advanced it only affects the selection of quantum gates. Af- classical search techniques in applications that use ter all the gates are applied, quantum measure- parallel processing3 or exploit problem structure, ment is performed, producing the algorithm’s often implicitly. Despite its promise, though, it is nonquantum output. Deutsch’s algorithm, for in- by no means clear whether, or how soon, quantum stance, solves a certain artificial problem in fewer computing methods will offer better performance steps than any classical (nonquantum) algorithm, in useful applications.4 Traditional complexity and its relative speedup grows with the problem analysis of Grover’s algorithm doesn’t consider the size. [Show full text]
- COVID-19 Detection on IBM Quantum Computer with Classical-Quantum Transfer Learning medRxiv preprint doi: https://doi.org/10.1101/2020.11.07.20227306; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Turk J Elec Eng & Comp Sci () : { © TUB¨ ITAK_ doi:10.3906/elk- COVID-19 detection on IBM quantum computer with classical-quantum transfer learning Erdi ACAR1*, Ihsan_ YILMAZ2 1Department of Computer Engineering, Institute of Science, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey 2Department of Computer Engineering, Faculty of Engineering, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey Received: .201 Accepted/Published Online: .201 Final Version: ..201 Abstract: Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. [Show full text]
- Quantum Inductive Learning and Quantum Logic Synthesis Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 2009 Quantum Inductive Learning and Quantum Logic Synthesis Martin Lukac Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Electrical and Computer Engineering Commons Let us know how access to this document benefits ou.y Recommended Citation Lukac, Martin, "Quantum Inductive Learning and Quantum Logic Synthesis" (2009). Dissertations and Theses. Paper 2319. https://doi.org/10.15760/etd.2316 This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. For more information, please contact [email protected] . QUANTUM INDUCTIVE LEARNING AND QUANTUM LOGIC SYNTHESIS by MARTIN LUKAC A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in ELECTRICAL AND COMPUTER ENGINEERING. Portland State University 2009 DISSERTATION APPROVAL The abstract and dissertation of Martin Lukac for the Doctor of Philosophy in Electrical and Computer Engineering were presented January 9, 2009, and accepted by the dissertation committee and the doctoral program. COMMITTEE APPROVALS: Irek Perkowski, Chair GarrisoH-Xireenwood -George ^Lendaris 5artM ?teven Bleiler Representative of the Office of Graduate Studies DOCTORAL PROGRAM APPROVAL: Malgorza /ska-Jeske7~Director Electrical Computer Engineering Ph.D. Program ABSTRACT An abstract of the dissertation of Martin Lukac for the Doctor of Philosophy in Electrical and Computer Engineering presented January 9, 2009. Title: Quantum Inductive Learning and Quantum Logic Synhesis Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. [Show full text]
- Nearest Centroid Classification on a Trapped Ion Quantum Computer www.nature.com/npjqi ARTICLE OPEN Nearest centroid classification on a trapped ion quantum computer ✉ Sonika Johri1 , Shantanu Debnath1, Avinash Mocherla2,3,4, Alexandros SINGK2,3,5, Anupam Prakash2,3, Jungsang Kim1 and Iordanis Kerenidis2,3,6 Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data. npj Quantum Information (2021) 7:122 ; https://doi.org/10.1038/s41534-021-00456-5 INTRODUCTION Thus, one might hope that noisy quantum computers are 1234567890():,; Quantum technologies promise to revolutionize the future of inherently better suited for machine learning computations than information and communication, in the form of quantum for other types of problems that need precise computations like computing devices able to communicate and process massive factoring or search problems. amounts of data both efficiently and securely using quantum However, there are significant challenges to be overcome to resources. Tremendous progress is continuously being made both make QML practical. [Show full text]
- A Functional Architecture for Scalable Quantum Computing A Functional Architecture for Scalable Quantum Computing Eyob A. Sete, William J. Zeng, Chad T. Rigetti Rigetti Computing Berkeley, California Email: feyob,will, [email protected] Abstract—Quantum computing devices based on supercon- ducting quantum circuits have rapidly developed in the last few years. The building blocks—superconducting qubits, quantum- limited amplifiers, and two-qubit gates—have been demonstrated by several groups. Small prototype quantum processor systems have been implemented with performance adequate to demon- strate quantum chemistry simulations, optimization algorithms, and enable experimental tests of quantum error correction schemes. A major bottleneck in the effort to devleop larger systems is the need for a scalable functional architecture that combines all thee core building blocks in a single, scalable technology. We describe such a functional architecture, based on Fig. 1. A schematic of a functional layout of a quantum computing system. a planar lattice of transmon and fluxonium qubits, parametric amplifiers, and a novel fast DC controlled two-qubit gate. kBT should be much less than the energy of the photon at the Keywords—quantum computing, superconducting integrated qubit transition energy ~!01. In a large system where supercon- circuits, computer architecture. ducting circuits are packed together, unwanted crosstalk among devices is inevitable. To reduce or minimize crosstalk, efficient I. INTRODUCTION packaging and isolation of individual devices is imperative and substantially improves the performance of the system. The underlying hardware for quantum computing has ad- vanced to make the design of the first scalable quantum Superconducting qubits are made using Josephson tunnel computers possible. Superconducting chips with 4–9 qubits junctions which are formed by two superconducting thin have been demonstrated with the performance required to electrodes separated by a dielectric material. [Show full text]
- Quantum Algorithms An Introduction to Quantum Algorithms Emma Strubell COS498 { Chawathe Spring 2011 An Introduction to Quantum Algorithms Contents Contents 1 What are quantum algorithms? 3 1.1 Background . .3 1.2 Caveats . .4 2 Mathematical representation 5 2.1 Fundamental differences . .5 2.2 Hilbert spaces and Dirac notation . .6 2.3 The qubit . .9 2.4 Quantum registers . 11 2.5 Quantum logic gates . 12 2.6 Computational complexity . 19 3 Grover's Algorithm 20 3.1 Quantum search . 20 3.2 Grover's algorithm: How it works . 22 3.3 Grover's algorithm: Worked example . 24 4 Simon's Algorithm 28 4.1 Black-box period finding . 28 4.2 Simon's algorithm: How it works . 29 4.3 Simon's Algorithm: Worked example . 32 5 Conclusion 33 References 34 Page 2 of 35 An Introduction to Quantum Algorithms 1. What are quantum algorithms? 1 What are quantum algorithms? 1.1 Background The idea of a quantum computer was first proposed in 1981 by Nobel laureate Richard Feynman, who pointed out that accurately and efficiently simulating quantum mechanical systems would be impossible on a classical computer, but that a new kind of machine, a computer itself \built of quantum mechanical elements which obey quantum mechanical laws" [1], might one day perform efficient simulations of quantum systems. Classical computers are inherently unable to simulate such a system using sub-exponential time and space complexity due to the exponential growth of the amount of data required to completely represent a quantum system. Quantum computers, on the other hand, exploit the unique, non-classical properties of the quantum systems from which they are built, allowing them to process exponentially large quantities of information in only polynomial time. [Show full text]
- Experimental Kernel-Based Quantum Machine Learning in Finite Feature www.nature.com/scientificreports OPEN Experimental kernel‑based quantum machine learning in fnite feature space Karol Bartkiewicz1,2*, Clemens Gneiting3, Antonín Černoch2*, Kateřina Jiráková2, Karel Lemr2* & Franco Nori3,4 We implement an all‑optical setup demonstrating kernel‑based quantum machine learning for two‑ dimensional classifcation problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer. Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space. In order to maximize the application range of the deployable kernels, we optimize feature maps towards the resulting kernels’ ability to separate points, i.e., their “resolution,” under the constraint of fnite, fxed Hilbert space dimension. Implementing these kernels, our setup delivers viable decision boundaries for standard nonlinear supervised classifcation tasks in feature space. We demonstrate such kernel-based quantum machine learning using specialized multiphoton quantum optical circuits. The deployed kernel exhibits exponentially better scaling in the required number of qubits than a direct generalization of kernels described in the literature. Many contemporary computational problems (like drug design, trafc control, logistics, automatic driving, stock market analysis, automatic medical examination, material engineering, and others) routinely require optimiza- tion over huge amounts of data1. While these highly demanding problems can ofen be approached by suitable machine learning (ML) algorithms, in many relevant cases the underlying calculations would last prohibitively long. Quantum ML (QML) comes with the promise to run these computations more efciently (in some cases exponentially faster) by complementing ML algorithms with quantum resources. [Show full text]
- High Energy Physics Quantum Computing High Energy Physics Quantum Computing Quantum Information Science in High Energy Physics at the Large Hadron Collider PI: O.K. Baker, Yale University Unraveling the quantum structure of QCD in parton shower Monte Carlo generators PI: Christian Bauer, Lawrence Berkeley National Laboratory Co-PIs: Wibe de Jong and Ben Nachman (LBNL) The HEP.QPR Project: Quantum Pattern Recognition for Charged Particle Tracking PI: Heather Gray, Lawrence Berkeley National Laboratory Co-PIs: Wahid Bhimji, Paolo Calafiura, Steve Farrell, Wim Lavrijsen, Lucy Linder, Illya Shapoval (LBNL) Neutrino-Nucleus Scattering on a Quantum Computer PI: Rajan Gupta, Los Alamos National Laboratory Co-PIs: Joseph Carlson (LANL); Alessandro Roggero (UW), Gabriel Purdue (FNAL) Particle Track Pattern Recognition via Content-Addressable Memory and Adiabatic Quantum Optimization PI: Lauren Ice, Johns Hopkins University Co-PIs: Gregory Quiroz (Johns Hopkins); Travis Humble (Oak Ridge National Laboratory) Towards practical quantum simulation for High Energy Physics PI: Peter Love, Tufts University Co-PIs: Gary Goldstein, Hugo Beauchemin (Tufts) High Energy Physics (HEP) ML and Optimization Go Quantum PI: Gabriel Perdue, Fermilab Co-PIs: Jim Kowalkowski, Stephen Mrenna, Brian Nord, Aris Tsaris (Fermilab); Travis Humble, Alex McCaskey (Oak Ridge National Lab) Quantum Machine Learning and Quantum Computation Frameworks for HEP (QMLQCF) PI: M. Spiropulu, California Institute of Technology Co-PIs: Panagiotis Spentzouris (Fermilab), Daniel Lidar (USC), Seth Lloyd (MIT) Quantum Algorithms for Collider Physics PI: Jesse Thaler, Massachusetts Institute of Technology Co-PI: Aram Harrow, Massachusetts Institute of Technology Quantum Machine Learning for Lattice QCD PI: Boram Yoon, Los Alamos National Laboratory Co-PIs: Nga T. T. Nguyen, Garrett Kenyon, Tanmoy Bhattacharya and Rajan Gupta (LANL) Quantum Information Science in High Energy Physics at the Large Hadron Collider O.K. [Show full text]
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The Future Directions Workshop series, sponsored by the Basic Research Office of the Office of the Under Secretary of Defense for Research and Engineering, seeks to examine emerging research and engineering areas that are most likely to transform future technology capabilities. Rather than a standard conference format, these workshops are ...
This report summarizes the major insights and themes that resulted from the two-day workshop. On August 25-26, the "Future Directions of Quantum Information Processing Workshop" was held at Arlington Va. It gathered 25 distinguished experts from academia, industry and government to discuss and debate the future of the emerging field of ...
The Future Directions in Toxicology Workshop convened on April 28-29, 2022, in Arlington, VA, to examine research challenges and opportunities to usher toxicology into a new paradigm as a predictive science. Hosted by the Basic Research Office in the Office of the Under Secretary of Defense for Research
collaboration on QIS research and development. On May 24-25, 2022, the Basic Research Office of the U.S. Department of Defense (DoD) and the Defence Science and Technology Group of Australia jointly sponsored the Future Directions Workshop (FDW) on Quantum Information ScienceBoth countries consider QIS a critical technology area for . both
The Future Directions Workshop series, sponsored by the Basic Research Office in the Office of the Under Secretary of Defense for Research and Engineering, seeks to examine emerging research areas that-in the natural and formal sciences-are most likely to transform future technology capabilities and-in the social sciences-innovatively inform about how the social world works.
In May of 2022, the Basic Research Office of the U.S. Department of Defense and the Defense Science and Technology Group of Australia jointly sponsored the Future Directions Workshop on Quantum Information Science (QIS). In attendance at the workshop were U.S. and Australian scientists selected from a wide range of QIS disciplines as well as a ...
This report is the product of a workshop held August 25-26, 2016, 2016 at research is the key to the Basic Research Innovation and Collaboration Center in Arlington, VA on the Future Directions of Quantum Information Processing. It is intended as a resource to the S&T community including the broader federal funding future innovation. community ...
Future Directions Workshop - Materials, Processes, and R&D Challenges in Microelectronics. The Basic Research Office runs Future Directions Workshops (FDW) with leaders of a field to create a roadmap of basic research advances necessary to move the field forward . The FDW report on Materials,
Materials, Processes, and R&D Challenges in Microelectronics
The Future Directions Workshop series, sponsored by the Basic Research Office of the Office of the Assistant Secretary of Defense for Research and Engineering, seeks to examine emerging research and engineering areas that are most likely to transform future technology capabilities.
The Future Directions Workshop series, sponsored by the Basic Research Directorate of the Office of the Under Secretary of Defense for Research and Engineering, seeks to examine emerging research and engineering areas that are most likely to transform future technology capabilities. These workshops gather
DoD Future Directions Workshop: Materials, Processes and R&D Challenges in Microelectronics ... Basic Research Office. June 23-24, 2022. Growing US chip output an 'expensive exercise in futility', warns TSMC founder. ... 2022. OSTP Report on the Industries of the Future Act. Office of Science and Technology Policy. April 13, 2022. DoD ...
The Future Directions in Quantum Information Processing Workshop sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering (ASDR&E) will be held on August 25-26, 2016 at the Basic Research Innovation and Collaboration Center (BRICC). ... This workshop is by invitation only. To accept the ...
This report is based on a SC/FE workshop on Carbon Capture: Beyond 2020, held March 4-5, 2010, to assess the basic research needed to address the current technical bottlenecks in carbon capture processes and to identify key research priority directions that will provide the foundations for future carbon capture technologies.
Technical Report: Basic Research Needs for Future Nuclear Energy: ... Based on the discussions held at the workshop, five Priority Research Directions (PRDs) were identified. These PRDs defined the key scientific challenges that must be met as well as the opportunities that can be exploited to achieve a multi-scale spatial and temporal ...
To accelerate the progress of quantum materials research, the U.S. Department of Energy's Office of Science, Office of Basic Energy Sciences (BES), sponsored a "Basic Research Needs Workshop on Quantum Materials for Energy-relevant Technology," which was held near Washington, D.C. on February 8-10, 2016.
Today's research, tomorrow's breakthroughs. ... Basic Research
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The Future Directions Workshop series, spon-sored by the Basic Research Office in the Office of the Under Secretary of Defense for Research and Engineering, seeks to ex-amine such emerging research areas to uncover new phenomena and generate new knowledge that are most likely to transform
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On July 16-17, 2019, a Future Directions of Human Machine Teaming workshop was held at the Basic Research Innovation Collaboration Center in Arlington, VA to examine the basic research challenges and opportunities to enable this level of human machine teaming. Hosted by the Basic Research Office in the Office of the Under Secretary of Defense ...
Sheremetyevo International Airport (SVO/UUEE) is an international airport located in Khimki, Moscow Oblast.Sheremetyevo serves as the main hub for Russian flag carrier Aeroflot and its branch Rossiya Airlines, Nordwind Airlines or Ural Airlines. The product is equipped with an automatic installer, which means that the scenery will be ...