REU Site: Quantum Machine Learning Algorithm Design and Implementation

量子计算（QC）有望加速信息处理并解决高度复杂的海量数据问题。这个为期三年的REU站点每年夏天将招募和培训9名本科生，并让他们从事量子信号处理和量子机器学习电路和模拟设计的研究工作。研究人员将与一组教师顾问一起，监督量子人工智能和量子数字信号处理（DSP）的一系列多学科研究项目。除了计划中的REU项目外，该项目的研究人员还将为学生组织一系列产学研合作培训活动。该REU具有跨不同研究实验室的多学科协同作用，提供独特的量子模拟软件，量子物理和网络设施，以及量子机器学习电路设计，用于包括健康，可持续性和安全性在内的多种应用。具体应用包括音频识别、图像理解、加密和太阳能系统。该项目还将包括跨领域的专业发展、公共演讲、政策、道德、专利开发和推广方面的模块和讲习班。年度REU研讨会将培训学生与利益相关者沟通。研究团队将使用美国国家科学基金会教育和培训申请（ETAP）系统招募REU学生参与者。地方和国家的评估单位，包括评估研究管道中心（CERP）将被部署进行评估，为项目改进提供反馈。本地站点评估人员还将根据学生参与者、学术和行业导师以及其他利益相关者的反馈，每年评估REU的目标。该项目让服务少数族裔的机构和专业学生分会参与进来，以扩大参与和加强招生。REU将解决与量子信息处理（QIP），特别是量子信号处理和量子机器学习（QML）相关的STEM问题。关键的研究和教育问题包括a)理解量子比特（Qubits）的理论和统计，b)量子噪声模型的介绍，c)理解量子比特精度和量子噪声之间的权衡，d)编程量子模拟的技能建设，以及e)实验室访问独特的QC设施。学院调查人员将组织项目和指导活动，包括REU学生的行业合作伙伴指导。该网站的目标是：a)通过让学生沉浸在政府和行业项目中，向他们介绍研究实践；b)让学生参与量子机器学习研究；c)激励学生追求QIP研究事业，并招募他们参加研究生课程；d)提供演示、道德和标准方面的跨领域技能。REU项目旨在向学生介绍一系列量子信息处理技术，强调量子模拟电路的设计，用于：基于人工智能的信号和数据分类、使用量子傅里叶变换的信号分析合成、量子云和边缘计算、量子网络、量子图像理解和基于量子的加密。在同一时期，项目将训练REU学生了解量子噪声和量子精度，量子比特（量子位）测量方法以及叠加和纠缠的理论方面的问题。REU将通过若干机制实现社会影响，包括跨领域培训、公共演讲和道德讲习班、量子项目成果的传播和外展。该奖项反映了美国国家科学基金会的法定使命，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。

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.