Quantum Machine Learning Online Materials and Software Modules for Undergraduate Education

该项目旨在通过向本科生介绍量子计算（QC），为QC中的劳动力开发建立基础和软件工具，并在本科阶段的电气工程和其他STEM课程中实施这些工具来服务于国家利益。该项目计划开发在线内容，模块和交互式软件，向本科生介绍量子计算，重点是量子机器学习（QML）。该项目团队将吸引电气和计算机工程专业的本科生以及其他STEM领域的学生。该项目还将包括在夏季为本科生（REU）提供以劳动力为中心的研究经验。该团队还将在夏季为STEM教师提供研究经验。为量子信息系统开发的内容将适用于不同的群体，包括本科生，REU学生和高中外展。赠款活动和目标还包括发展多样化的用户社区、创新的视频流内容、用于技能建设的互动软件和夏季培训讲习班。创建的材料将：a）影响几个STEM学科，B）吸引和激励本科生，c）创造有影响力的量子信息科学意识和入门技能，d）建立量子信息系统劳动力发展的模块和工具。该项目的动机是国家需要发展量子计算的劳动力，重点是QML。该项目团队将开发和全面评估几个QML产品的本科课程和培训，包括广泛访问的在线材料和交互式软件。开发的材料和模块将支持信号处理和机器学习的高级选修课程。该项目将向本科生介绍量子计算和QML，使用应用驱动的材料和交互式软件。评估将由学院研究和评估服务团队（CREST）处理。最后，该项目寻求通过若干战略扩大参与，包括与少数民族学生分会和少数民族服务机构合作，以及利用国际大学合作进行全球传播。具体产品包括用于量子机器学习和量子傅立叶变换的交互式分析和可视化软件工具。这些工具将使学生能够理解和实验量子参数，并评估它们在语音识别等引人注目的应用中的效果。评估小组将评估所有模块，其吸引学生的能力，扩大参与的能力，以及QML材料和交互式软件在劳动力发展中的整体有效性。该项目将使用混合方法评估过程（定性和定量数据收集），以了解使用量子计算工具对学生学习收益的影响。评估将通过电子网络工具，前和后测验，演示文稿，一对一的访谈，和普通的课堂测试。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。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。

This project aims to serve the national interest by introducing undergraduate students to Quantum Computing (QC), establishing foundations and software tools for workforce development in QC, and implementing these tools in Electrical Engineering and other STEM courses at the undergraduate level. The project plans to develop online content, modules and interactive software to introduce undergraduate students to Quantum computing with emphasis on quantum machine learning (QML). The project team will engage undergraduate students in Electrical and Computer Engineering as well as students from other STEM areas. The project will also include workforce-focused research experiences for undergraduate (REU) students during the summers. The team will also include a research experience for STEM teachers during the summer. The content developed for quantum information systems will be adapted for different groups, including undergraduate students, REU students, and for high school outreach. Grant activities and objectives also include developing a diverse community of users, innovative video-streamed content, interactive software for skill building, and summer training workshops. The materials created will: a) impact several STEM disciplines, b) engage and energize undergraduate students, c) create impactful quantum information science awareness and introductory skills, and d) establish modules and tools for workforce development in quantum information systems. This project is motivated by the national need to develop a workforce in quantum computing with emphasis on QML. The project team will develop and thoroughly assess several QML products for undergraduate courses and training, including widely accessible online materials and interactive software. Materials and modules developed will support senior level elective courses in signal processing and machine learning. The project will introduce undergraduate students to Quantum computing and QML, using application-driven materials and interactive software. Assessment will be handled by the College Research and Evaluation Services Team (CREST). Finally, the project seeks to broaden participation through several strategies including collaborations with minority student chapters and minority serving institutions, and the leveraging of international university collaborations for global dissemination. Specific products include interactive analysis and visualization software tools for quantum machine learning and quantum Fourier transforms. These tools will enable students to understand and experiment with quantum parameters and assess their effect in compelling applications such as voice recognition. The assessment team will evaluate all the modules, their ability to engage students, capabilities in broadening participation, and the overall effectiveness of QML materials and interactive software in workforce development. The project will use a mixed-method assessment process (qualitative and quantitative data collection) to build an understanding of the impact of the use of the quantum computing tools on student learning gains. Assessments will be done through electronic web tools, pre- and post-quizzes, presentations, one-to-one interviews, and ordinary in-class testing. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.