This paper introduces a hybrid two-dimensional Quantum Fourier Transform (2D hybrid QFT) method for image analysis and synthesis. The QFT and Inverse QFT (IQFT) functions enable analysis-synthesis implementation using frequency spectrum-based selection approaches for compression. We study the resolution and precision of the QFT by comparing its outputs against classical two-dimensional Fast Fourier Transform (FFT) techniques using pixel-level Signal to Noise Ratio (SNR) as a metric. Formative and summative assessments have been deployed as a laboratory exercise in two National Science Foundation (NSF) workforce development programs. Preliminary evaluation results are presented in this paper.

This work in progress research-to-practice study describes the development of a new undergraduate research training site on Quantum Machine Learning (QML), hosted at Arizona State University, a large Hispanic-Serving Institution. The objectives of this project are to a) recruit and prepare students from diverse pathways to increase representation of those traditionally underrepresented in QML research, b) increase awareness of career opportunities in the QML field, c) engage students in theoretical and experimental quantum information processing and machine learning (ML), d) motivate students to continue QML research into graduate school, and e) provide professional development training including presenting to stakeholders, developing publications/patents, and building an awareness on social implications, ethics, and privacy. The project adopts an integrative theory, application, and hands-on training approach by immersing undergraduate students in ML algorithm and quantum computing studies with hands-on quantum circuit design tasks. Participants are embedded in research labs, guided by graduate students and faculty mentors on quantum computing research studies. The program is evaluated by both the Center for Evaluating the Research Pipeline (CERP) and an independent evaluator. Formative and summative assessments include pre- and post-surveys, a mid-point check-in survey, and a document review of program deliverables. Findings are described in a final evaluation report. This paper describes the importance of introducing QML research at the undergraduate level, methods for recruiting a diverse group of participants, program format, research projects, and preliminary program evaluation results.

This Innovative Practice Work in Progress Paper describes the development and assessment of a web-based simulation lab exercise introducing basic quantum computing concepts in a sophomore signals and systems course. Specifically, students make the connection between quantum computing and signals and systems theory through a comparative study of using Quantum Fourier Transforms and Fast Fourier Transforms for a speech analysis-synthesis application. In addition, quantum noise models are introduced and simulated to show their effect on computation performance. Statistics from pre/post quizzes show that there is significant knowledge improvement by completing the lab exercise.

This Work-In-Progress paper describes a program in quantum machine learning launched in the academic year of 2021-22. The program engaged undergraduate students from STEM areas with faculty and industry mentors. Because of the COVID-19 conditions, this undergraduate engagement was offered in a virtual format. In 2022, some face-to-face meetings with presentations were also held. The program included: a) training in machine learning with quantum simulators, b) weekly presentations, and c) semester end presentations. The assessment of the program included surveys, interviews, and presentation observations. Challenges and opportunities from virtual engagement were also part of the assessment.

Machine learning and Artificial Intelligence (AI) are national priority areas for research, education and workforce development. This work in progress paper describes a Research Experiences for Teachers program in sensors and machine learning launched in the summer of 2020. Motivated by national AI workforce needs, we designed a program that engaged high school teachers from STEM fields in machine learning research. In 2020, the program focused on AI algorithms for solar energy systems. Because of the COVID-19 conditions, the research experience was virtual and ran with a smaller teacher group than originally planned. The program included development of training content, algorithm and software training, research in solar energy monitoring, development of research reports and lesson plans, research presentations, and assessment. The assessment of the program included surveys, interviews, presentation observations, and follow-up in high school content delivery.

This Innovative Practice Work in Progress paper presents the collaborative efforts made by three NSF-funded Engineering Research Centers (ERCs) to synthesize common tools for educational program evaluation. The aim of the NSF ERCs is to achieve transformative changes by integrating engineering research and education with technological innovation within areas at the frontiers of science and engineering (e.g., NSF's 10 Big Ideas). Such centers across the nation study and innovate within their technical area using similar structures and implementation strategies, including the coordination of educational endeavors. Independent partners are enlisted as part of these centers to evaluate education and diversity impacts annually. Each center typically performs this task in isolation from other such centers. The effort required to create resources for such evaluation outcome can result in redundancy and an inability for psychometric analysis due to small available populations within a single center. This paper elaborates on the ongoing efforts of this collaborative research aimed at addressing these issues by creating a streamlined, customizable, and standardized set of evaluation instruments that can be applied to any ERC evaluation.