NSF Org: |
OSI Office of Strategic Initiatives (OSI) |
Recipient: |
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Initial Amendment Date: | August 23, 2022 |
Latest Amendment Date: | February 9, 2024 |
Award Number: | 2231377 |
Award Instrument: | Standard Grant |
Program Manager: |
Wu He
wuhe@nsf.gov (703)292-0000 OSI Office of Strategic Initiatives (OSI) MPS Direct For Mathematical & Physical Scien |
Start Date: | September 1, 2022 |
End Date: | August 31, 2025 (Estimated) |
Total Intended Award Amount: | $799,985.00 |
Total Awarded Amount to Date: | $799,985.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
2121 EUCLID AVE CLEVELAND OH US 44115-2214 (216)687-3630 |
Sponsor Congressional District: |
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Primary Place of Performance: |
2121 Euclid Avenue Cleveland OH US 44115-2214 |
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): | OFFICE OF MULTIDISCIPLINARY AC |
Primary Program Source: |
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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
Non-technical Description:
The project aims to create and evaluate quantum and classical reinforcement learning-based agents for the optimal design of programmable quantum sensor circuit. The resulting technology from this research project will allow for better meters of the physical world with a breadth of applications that bridge many fields of science. The outcomes from this project will have a positive impact on the precision quantum-enhanced metrology measurements systems, such as the Noisy Intermediate Scale Quantum devices. The project will provide rich opportunities for Quantum Information Science and Engineering (QISE) research training and professional development. The project team will recruit, motivate, and train diverse and underrepresented minority and female students in QISE research methods. The project will have a positive impact on the K?16 QISE talent development pipeline and workforce development for the city of Cleveland, where underrepresented minorities such as African Americans and Hispanics constitute the majority of the population.
Technical Description:
Quantum sensing is a mature technology that has achieved remarkable progress over the past decades. The challenge going forward is to leverage potential gains from quantum entanglement and superposition to enable the next generation of sensors and thereby narrow the gap between the current performance and the fundamental limits set by quantum physics. However, the optimal design of a quantum sensor circuit that generates entangled qubits is a non-trivial task, which motivates the consideration of machine learning to assist with this design. Current efforts have in large part been limited to variational optimization of few parameter systems corresponding to simple circuits with few elements. To advance the state of the art, in this project, a reinforcement-learning-based optimal circuit design is developed for programmable quantum sensors. The specific objective is to create and evaluate quantum and classical reinforcement learning-based agents to design the deep circuit. The method utilizes a learning cycle of actions and rewards to generate the sequence of gates with optimal performance, using the measure of quantum Fisher information as a means to quantify the reward. The methodology involves multiple components such as demonstrations of the ideal system, evaluation of noise and imperfections, extensions to a quantum agent, and the performance evaluation of classical and quantum agents towards the design of the programmable quantum sensor circuit. Metrics used to evaluate the success of the research approach include sensitivity, dynamic range, robustness to dissipation and decoherence, and speed.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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