REU Site: Quantum Machine Learning Algorithm Design and Implementation

REU 站点：量子机器学习算法设计与实现

• 批准号: 2349567
• 负责人: Andreas Spanias
• 金额: $ 35.79万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2349567&HistoricalAwards=false
• 关键词: REU; Site; Quantum; Machine; Learning

Quantum Computing (QC) promises to accelerate information processing and solve highly complex massive data problems. This three-year REU site will recruit and train nine undergraduate students each summer and engage them in research endeavors on the design of quantum signal processing and quantum machine learning circuits and simulations. The investigators, along with a team of faculty advisors, will supervise a series of multidisciplinary research projects in quantum AI and quantum Digital Signal Processing (DSP). In addition to the planned REU projects, the investigators of this project will organize a series of industry-university collaborative training activities for the students. This REU features multidisciplinary synergies across different research labs that provide access to unique quantum simulation software, quantum physics and networking facilities, and quantum machine learning circuit design for several applications including health, sustainability, and security. Specific applications include audio recognition, image understanding, encryption and solar energy systems. The program will also include crosscutting professional development, modules and workshops in public speaking, policy, ethics, patent development and outreach. Annual REU workshops will train students to communicate with stakeholders. The investigator team will use the NSF Education and Training Application (ETAP) system for recruitment of REU student participants. Local and national evaluation units including the Center for Evaluating the Research Pipeline (CERP) will be deployed for assessments that will provide feedback for program improvement. Local site evaluators will also assess REU goals annually using feedback from student participants, academic and industry mentors, and other stakeholders. The program engages minority-serving institutions and professional student chapters to broaden participation and enhance recruitment.The REU will address STEM problems associated with quantum information processing (QIP) and specifically quantum signal processing and quantum machine learning (QML). Key research and education problems include a) understanding the theory and statistics of Quantum bits (Qubits), b) introduction to quantum noise models, c) understanding of tradeoffs between Qubit precision and quantum noise, d) skill-building with programming quantum simulations, and e) laboratory access to unique QC facilities. The faculty investigators will organize project and mentorship activities including REU student mentorship by industry partners. The objectives of the proposed site are to a) introduce students to research practices by immersing them in government and industry projects, b) engage students in quantum machine learning research, c) motivate students to pursue QIP research careers and recruit them to graduate programs, and d) provide cross-cutting skills in presentation, ethics, and standards. The REU projects are designed to introduce students to an array of quantum information processing technologies that emphasize the design of quantum simulation circuits for: AI-based signal and data classification, signal analysis synthesis using the quantum Fourier transform, quantum cloud and edge computing, quantum networking, quantum image understanding, and quantum based encryption. During the same period, projects will train REU students to understand issues dealing with quantum noise and quantum precision, quantum bit (qubit) measurement methods and theoretical aspects of superposition and entanglement. The REU will achieve social impact through several mechanisms including cross-cutting training, workshops on public speaking and ethics, dissemination of quantum project results and outreach.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.

量子计算（QC）有望加速信息处理并解决高度复杂的大规模数据问题。这个为期三年的REU网站将每年夏天招募和培训九名本科生，并参与研究量子信号处理以及量子机器学习电路和模拟的研究。调查人员与教职顾问团队一起将监督一系列量子AI和量子数字信号处理（DSP）的多学科研究项目。 除了计划的REU项目外，该项目的调查人员还将为学生组织一系列行业 - 大学合作培训活动。此REU具有不同研究实验室的多学科协同作用，可访问独特的量子模拟软件，量子物理和网络设施以及针对多种应用，包括健康，可持续性和安全的量子机器学习电路设计。 特定的应用包括音频识别，图像理解，加密和太阳能系统。 该计划还将包括在公开演讲，政策，道德，专利发展和宣传方面的专业发展，模块和讲习班。年度REU研讨会将培训学生与利益相关者进行沟通。研究人员团队将使用NSF教育和培训应用程序（ETAP）系统来招募REU学生参与者。 包括评估研究管道（CERP）在内的本地和国家评估单位将用于评估，以提供计划改进的反馈。本地现场评估人员还将使用学生参与者，学术和行业导师以及其他利益相关者的反馈每年评估REU目标。该计划与少数族裔服务机构和专业学生章节互动，以扩大参与并增强招聘。REU将解决与量子信息处理（QIP）相关的STEM问题（QIP），特别是量子信号处理和量子机器学习（QML）。关键的研究和教育问题包括a）了解量子位的理论和统计数据（Qubits），b）量子噪声模型简介，c）理解量子精度和量子噪声之间的权衡，d）使用编程量子模拟的技能构建，以及e）实验室访问独特QC设施。教师调查人员将组织项目和指导活动，包括行业合作伙伴的REU学生指导。拟议网站的目标是a）介绍学生通过将他们浸入政府和行业项目中，b）让学生参与量子机器学习研究，c）激励学生从事QIP研究职业并招募他们以研究生课程，d）d）在演讲，伦理学，伦理学和标准方面提供交叉挑战技能。 REU项目旨在向学生介绍一系列量子信息处理技术，这些技术强调了：基于AI的信号和数据分类，使用量子傅立叶变换，量子云和边缘计算，量子网络，量子网络，量子图像理解和基于量子的加密来综合量子模拟电路，信号分析合成。在同一时期，项目将培训REU学生了解有关量子噪声和量子精度，量子位（Qubit）测量方法和叠加和纠缠的理论方面的问题。 REU将通过多种机制来实现社会影响，包括跨切割培训，公开演讲和道德的讲习班，量子项目成果的传播和推广。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。