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中的劳动力发展建立基础和软件工具，并在本科水平的电气工程和其他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、模块和互动软件，向本科生介绍量子计算，重点是量子机器学习(QML)。该项目团队将邀请电气和计算机工程专业的本科生以及来自STEM其他领域的学生参与。该项目还将包括为本科(REU)学生在暑假期间提供的以劳动力为中心的研究体验。该团队还将在暑期为STEM教师提供研究体验。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、用于技能培养的互动软件和暑期培训讲习班。创建的材料将：a)影响几个STEM学科，b)吸引和激励本科生，c)培养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。这个项目的动机是国家需要发展一支量子计算劳动力队伍，重点是QML。该项目团队将为本科课程和培训开发和彻底评估几个QML产品，包括广泛可用的在线材料和互动软件。编写的材料和模块将支持信号处理和机器学习方面的高级选修课。该项目将向本科生介绍量子计算和QML，使用应用程序驱动的材料和交互软件。评估将由学院研究和评估服务小组(CREST)处理。最后，该项目寻求通过若干战略扩大参与，包括与少数群体学生分会和少数群体服务机构合作，以及利用国际大学合作在全球传播。具体产品包括用于量子机器学习和量子傅里叶变换的交互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，它们吸引学生参与的能力，扩大参与的能力，以及QML材料和互动软件在劳动力发展方面的整体效果。该项目将使用混合方法评估过程(定性和定量数据收集)，以了解量子计算工具的使用对学生学习成果的影响。评估将通过电子网络工具、前后测验、演示文稿、一对一面试和普通课堂测试来完成。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.