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网站每年夏天将招募和培训9名本科生，并让他们参与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。研究人员沿着一组教师顾问，将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，可以访问独特的量子模拟软件，量子物理和网络设施，以及用于健康，可持续性和安全等多种应用的量子机器学习电路设计。 具体应用包括音频识别、图像理解、加密和太阳能系统。 该计划还将包括跨领域的专业发展，公开演讲，政策，道德，专利开发和推广的模块和研讨会。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NSF教育和培训应用（ETAP）系统招募REU学生参与者。 包括研究管道评估中心（CERP）在内的地方和国家评估单位将被部署进行评估，为项目改进提供反馈。当地的网站评估人员还将评估REU的目标，每年使用来自学生参与者，学术和行业导师，以及其他利益相关者的反馈。该计划涉及少数民族服务机构和专业学生分会，以扩大参与和提高招聘。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。主要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）介绍量子噪声模型，c）理解量子比特精度和量子噪声之间的权衡，d）通过编程量子模拟进行技能培养，e）实验室访问独特的QC设施。教师调查人员将组织项目和导师活动，包括行业合作伙伴的REU学生导师。拟议网站的目标是：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）提供跨领域的演讲，道德和标准技能。 REU项目旨在向学生介绍一系列量子信息处理技术，这些技术强调量子模拟电路的设计：基于AI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，项目将培训REU学生了解量子噪声和量子精度，量子比特（qubit）测量方法以及叠加和纠缠的理论方面的问题。REU将通过多个机制实现社会影响，包括跨领域培训、公开演讲和道德研讨会、量子项目成果传播和外展。该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估而被认为值得支持优点和更广泛的影响审查标准。

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.