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：量子传感是一种颠覆性的技术，已经在各个研究领域得到了应用。缺陷量子传感技术是量子传感的主要方法之一，其成功主要归功于金刚石中氮空位（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）目前对量子传感缺陷的研究能力。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。该项目还将通过在量子传感实验项目中培训少数民族学生，为量子信息科学与工程（QISE）劳动力的多样性做出贡献。更多的少数民族学生将通过MSU的新量子科学课程接受QISE概念和应用的培训。为期两周的暑期研讨会将组织培训至少八个少数民族服务K12教师每年如何教量子科学给他们的学生。将建立QISE证书计划。超过1000名K-12学生和家长将在每年的MSU STEM博览会上通过讲座和动手演示接触到量子科学概念。技术摘要：量子传感的温度，宿主材料应变，磁场和电场的极小变化成功地证明了与金刚石中的NV缺陷，其中光学检测磁共振（ODMR）方法是一个关键组成部分。然而，基于固态缺陷的量子传感器的电流灵敏度比预测的理论极限小几个数量级。一系列的连续波（CW）和脉冲ODMR协议的开发，以提高量子传感实验的检测限的缺陷。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，包括合奏中的电荷动力学，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，目前实验性的基于固态缺陷的量子传感器装置体积庞大，小尺寸版本尚未得到证明。人们对宽带隙半导体中的其他缺陷也越来越感兴趣，因为它们在量子传感应用中作为金刚石中NV缺陷的替代品。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，AC和谐振耦合ODMR协议和其他硬件添加来改进现有的设置。新的ML算法将为证明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，以获得具有高NV浓度的高质量金刚石样品。将首次展示基于固态缺陷的半导体集成电路量子传感器。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。该项目由历史上黑人学院和大学-本科生项目（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，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。