ExpandQISE: Track 1: Micron Scale Solid State Quantum Sensors Optimized through Machine Learning

ExpandQISE：轨道 1：通过机器学习优化微米级固态量子传感器

• 批准号: 2329242
• 负责人: Birol Ozturk
• 金额: $ 80万
• 依托单位: Morgan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329242&HistoricalAwards=false
• 关键词: ExpandQISE; Track; Micron; Scale; Solid

Non-technical Abstract: Quantum sensing is a disruptive technology that has already found applications in various research fields. Quantum sensing with defects is one of the leading approaches owing its success mostly to the room temperature operation capability of nitrogen vacancy (NV) color center defects in diamond. The project aims to expand the current research capabilities on quantum sensing with defects at Morgan State University (MSU). The outcomes of this project will accelerate the development of products that benefit the broader community directly. This project will also contribute to the diversity of Quantum Information Science and Engineering (QISE) workforce by training minority students in quantum sensing experimental projects. More minority students will be trained on QISE concepts and applications through new quantum science courses at MSU. Two weeks long summer workshops will be organized to train at least eight minority serving K12 teachers each year on how to teach quantum science to their students. A QISE certificate program will be established. Over a thousand K-12 students and parents will be exposed to quantum science concepts at the annual MSU STEM Expo via lectures and hands-on demonstrations.Technical Abstract: Quantum sensing of extremely small changes in temperature, host material strain, magnetic and electric fields was successfully demonstrated with NV defects in diamond, where optically detected magnetic resonance (ODMR) method is a key component. However, current sensitivities of solid-state defect-based quantum sensors are orders of magnitude less than the predicted theoretical limits. A range of continuous wave (CW) and pulsed ODMR protocols were developed for improving detection limits of quantum sensing experiments with defects. Machine learning (ML) algorithms have the potential to enhance the sensitivities of these quantum sensors. In addition, there are numerous aspects of NV physics, including charge dynamics in ensembles, that are still not well understood and thus require further research and exploration. Furthermore, current experimental solid-state defect-based quantum sensor setups are bulky and small footprint versions are yet to be demonstrated. There is also an increasing interest in other defects in wide bandgap semiconductors for their use in quantum sensing applications as alternatives to NV defects in diamond. The project team will collaborate with an expert in solid-state defect-based quantum sensors at the University of Chicago/Argonne National Laboratory to improve the existing setups by implementing pulsed, AC, and resonant coupling ODMR protocols and other hardware additions. New ML algorithms will pave the way to demonstrate enhanced sensitivities approaching predicted theoretical limits. Diamond growth and treatment methods will be established to obtain high-quality diamond samples with high NV concentrations. Micron-scale solid-state defect-based integrated circuit quantum sensors will be demonstrated for the first time. Extending the improved capabilities for characterization and device fabrication to other defects in wide bandgap semiconductors will advance the understanding of their properties and will facilitate their application in a wide range of quantum sensing applications.This project is jointly funded by the Historically Black Colleges and Universities - Undergraduate Program (HBCU-UP), the Office of Multidisciplinary Activities (MPS/OMA), and the Technology Frontiers Program (TIP/TF).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.

非技术摘要：量子传感是一项颠覆性技术，已在各个研究领域得到应用。缺陷量子传感是主要方法之一，其成功主要归功于钻石中氮空位 (NV) 色心缺陷的室温操作能力。该项目旨在扩大摩根州立大学（MSU）目前在缺陷量子传感方面的研究能力。该项目的成果将加速产品的开发，使更广泛的社区直接受益。该项目还将通过对少数族裔学生进行量子传感实验项目的培训，为量子信息科学与工程（QISE）劳动力的多样性做出贡献。更多少数族裔学生将通过密歇根州立大学新的量子科学课程接受 QISE 概念和应用的培训。每年将组织为期两周的夏季研讨会，培训至少八名在职 K12 少数族裔教师如何向学生教授量子科学。将建立 QISE 证书计划。超过 1000 名 K-12 学生和家长将在一年一度的 MSU STEM 博览会上通过讲座和实践演示接触到量子科学概念。技术摘要：对温度、主体材料应变、磁场和电场的极微小变化进行量子传感成功地证明了金刚石中的 NV 缺陷，其中光学检测磁共振 (ODMR) 方法是关键组成部分。然而，基于固态缺陷的量子传感器的当前灵敏度比预测的理论极限低几个数量级。开发了一系列连续波 (CW) 和脉冲 ODMR 协议，用于提高缺陷量子传感实验的检测限。机器学习 (ML) 算法有可能提高这些量子传感器的灵敏度。此外，NV物理学的许多方面，包括系综中的电荷动力学，仍然没有得到很好的理解，因此需要进一步的研究和探索。此外，当前实验性的基于缺陷的固态量子传感器装置体积庞大，且小足迹版本尚未得到证实。人们对宽带隙半导体中的其他缺陷也越来越感兴趣，因为它们可以作为金刚石中 NV 缺陷的替代品用于量子传感应用。该项目团队将与芝加哥大学/阿贡国家实验室的固态缺陷量子传感器专家合作，通过实施脉冲、交流和谐振耦合 ODMR 协议和其他硬件添加来改进现有设置。新的机器学习算法将为展示接近预测理论极限的增强灵敏度铺平道路。将建立金刚石生长和处理方法，以获得高 NV 浓度的高质量金刚石样品。微米级固态缺陷集成电路量子传感器将首次展示。将改进的表征和器件制造能力扩展到宽带隙半导体中的其他缺陷将促进对其特性的理解，并将促进其在广泛的量子传感应用中的应用。该项目由历史悠久的黑人学院和大学联合资助 -本科生计划 (HBCU-UP)、多学科活动办公室 (MPS/OMA) 和技术前沿计划 (TIP/TF)。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持基金会的智力价值和更广泛的影响审查标准。