| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | July 21, 2015 |
| Latest Amendment Date: | August 8, 2019 |
| Award Number: | 1528735 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Andrew Pollington
adpollin@nsf.gov (703)292-4878 DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2020 (Estimated) |
| Total Intended Award Amount: | $246,650.00 |
| Total Awarded Amount to Date: | $246,650.00 |
| Funds Obligated to Date: |
FY 2016 = $196,651.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
21 N PARK ST STE 6301 MADISON WI US 53715-1218 (608)262-3822 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
21 North Park Street Madison WI US 53715-1218 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Chem Thry, Mdls & Cmptnl Mthds, CISE-MPS QIS Faculty Program |
| Primary Program Source: |
01001617DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
For decades computer power has doubled for constant cost roughly once every two years according to the so-called Moore's law. This dream run is realized through technological advances in the fabrication of computer hardware, making electronic devices smaller and smaller. However, as the sizes of the computer electronic devices get close to the atomic scale, quantum effects are starting to interfere in their functioning, and thus conventional computer technology approaches run up against fundamental difficulties of size limit. This research project concerns quantum computing, the development of computer technology dependent on the principles of quantum physics, as opposed to the electronic devices following laws of classical physics used by classical computers. This revolutionary field will enable a range of exotic new devices, and in particular it will likely lead to the creation of powerful quantum computers. The research project is among the frontier research endeavors where quantum technologies are being developed and quantum devices are being built with capabilities exceeding those of classical computational devices.
Quantum computation and quantum information science more generally concern the preparation and control of the quantum states of physical systems to manipulate and transmit information. A quantum system usually has complexity exponentially increasing with its size. As a result, it takes an exponential number of bits of memory on a classical computer to store the state of a quantum system, and simulations of quantum systems via classical computers face great computational challenge. On the other hand, since quantum systems are able to store and track an exponential number of complex numbers and perform data manipulations and calculations as the systems evolve, quantum systems hold great promise as computational tools. Quantum information science grapples with understanding how to take advantage of the enormous amount of information hidden in the quantum systems and to harness the immense potential computational power of atoms and photons for the purpose of information processing and computation. This cross-disciplinary research project addresses questions in quantum information science on the interface between quantum computing and machine learning and between quantum tomography and compressed sensing. The collaborative research aims to explore the power of adiabatic quantum computation and its impact on computer science and statistics in general and machine learning and Monte Carlo sampling in particular. The work investigates the use of leading techniques from machine learning in computer science and statistics, compressed sensing in applied mathematics, statistics, and engineering, and quantum tomography in quantum physics for adiabatic quantum computing. The research activities promote collaborations among investigators with different disciplinary backgrounds and stimulate novel ideas for possible breakthrough, transformative research.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The NSF grant has served the purpose to facilitate the visit of the principal investigator (PI) in the Center for Quantum Information Science & Technology at the University of Southern California (USC) and interact with the researchers in the Center and the University. The PI has made many visits to USC and involved the cross-disciplinary Quantum Information Science (QIS) research on the interface between quantum annealing and machine learning and between quantum tomography and compressed sensing. The QIS research intends to explore the power of quantum computation and its impact on computer science and statistics in general and statistical machine learning and financial data science in particular. The study has investigated the use of leading techniques from machine learning in computer science and statistics, compressed sensing in applied mathematics and statistics as well as engineering, and quantum tomography in quantum physics for quantum computing. While the PI has been working on QIS from the statistics and machine learning background, the USC has the research environment of physics, chemistry and engineering with quantum computing center housing the D-Wave system. The cross-discipline and cross-institution research activities have broadened the PI's experience and reshaped his research vision and directions. The visit and interaction experiences have promoted and will continue to promote cross-disciplinary interaction and collaborations among researchers with different disciplinary backgrounds and stimulate novel ideas for possible breakthrough and potential transformative research.
Quantum information science studies quantum related theories and technologies to create quantum devices for the purpose of performing computation and processing information. Quantum computation performs calculations by using quantum devices instead of electronic devices following classical physics and used by classical computers. The quantum research has investigated quantum tomography that aims to determine the state of a quantum system and the time evolution of the state. It is important in quantum computation and quantum information to learn and engineer quantum systems. The research has developed mathematical and statistical modeling and analysis of quantum annealing and quantum annealers such as D-Wave computing hardware devices. The developed modeling and analysis methodologies have been used to analyze data from quantum computing devices for solving computing problems and data from classical and quantum based annealing models. The research has employed the proposed techniques based on Markov chain Monte Carlo simulations to investigate the quantum nature of the devices. The research work has explored the interface of quantum information science and machine learning as well as financial data science. The PI has engaged in the study of machine learning algorithms and financial statistics as well as their potential relationships with quantum information science. The research study has led to the investigation on what kind of statistical machine learning tasks can be attacked more efficiently by quantum computing.
Last Modified: 11/01/2020
Modified by: Yazhen Wang
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