Undergraduate Research and Education in Quantum Machine Learning
Glen S. Uehara, Jean S. Larson, Wendy Miedel Barnard, Michael Esposito, Filippo Posta, Maxwell Yarter, Aradhita Sharma, Niki Kyriacou, Matthew Dobson, Andreas Spanias
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.
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.
This study describes the Research Experiences for Undergraduate (REU) students training program on Quantum Machine Learning (QML), hosted by the SenSIP Center at Arizona State University. In 2025, the REU hosted several projects that engaged quantum computing in audio analysis, computer vision, medical imaging diagnostics, anomaly detection, and generative machine learning. The objectives of the research training program are to a) embed students in QML research by immersing them in government and industry projects, b) train students to program QML simulations, c) encourage students to pursue graduate research, d) increase awareness of career opportunities in QML, and e) provide professional development in presenting and documenting research. The paper describes the research projects of the students and presents preliminary assessments by external evaluators.
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.
Quantum Machine Learning (QML) emerges as a transformative approach to addressing the growing complexities of modern data processing and computational challenges. Classical machine learning (CML) techniques, while powerful, face limitations in handling vast amounts of high-dimensional data and solving complex optimization problems efficiently. Despite its potential, QML remains underrepresented in academia, highlighting the need for accessible, hands-on learning experiences and knowledgeable faculty. This project seeks to advance QML education by incorporating it into diverse curricula, creating practical learning materials, and fostering workforce readiness. Using Google Colab, an open source labware has been designed to provide interactive learning modules (M0 to M8), each addressing specific computational and industrial challenges. These modules cover key QML concepts, including Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNNs), and comparisons between QML and CML. Additionally, select modules demonstrate real-world applications of QML algorithms in areas such as predicting product back-orders, quality inspection in manufacturing, patient flow analysis in healthcare, and predictive maintenance for operational efficiency. To support the adoption of QML in academia, a faculty workshop was recently held with participants from universities in Georgia, Florida, and Missouri, representing fields such as Industrial and Systems Engineering, Computer Science, and Electrical Engineering. This workshop provided faculty with the tools and knowledge needed to integrate QML modules into their courses. A student workshop is scheduled for the end of March, offering hands-on experience and engagement with QML concepts. This project is shaping the future of QML education by providing accessible, hands-on learning experiences and fostering interdisciplinary collaboration. Through faculty and student workshops, it equips the next generation of professionals with the skills needed to drive advancements in QML across academia and industry.
Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.
AI-powered machine vision has become a widely adopted technology in fields such as quality control, healthcare, and facial recognition, making it a standard option for many applications. Despite its widespread adoption, it continues to face several challenges. The primary barriers to its broader use in certain fields include its reliance on large datasets, lengthy training times, and high computational costs. To address these challenges, researchers are exploring quantum computing, which has the potential to process complex computations more efficiently than classical methods. Quantum computing leverages principles such as superposition, entanglement, and quantum encoding, enabling exponential speedup in computation and parallel processing of high-dimensional data, such as images.Since this research field is still relatively new and multiple approaches are being explored simultaneously, this paper aims to provide the reader with a comprehensive understanding of the current state of Quantum and Hybrid Quantum Machine Learning. It analyzes the different methodologies of quantum machine learning and presents their advantages and limitations. This should enable the reader to assess future developments in this field and offer insights into potential industrial applications.
Quantum-based machine learning (QML) is known to integrate quantum computing (QC) and machine learning (ML) to boost performance in data analysis and decision-making. The demand for professionals in QML has increased; however, QML is not part of the curricula for most colleges and universities. In this project, we create labware in Google Colab that encompasses knowledge of QC, ML, and QML, and demonstrates QML applications for analyzing scientific and engineering data. We integrate our learning modules into courses in Computer Science, Industrial Engineering, Physics, and other disciplines, and host a faculty workshop, a student camp, and a conference tutorial session to promote the use of our learning modules. The feedback from participating faculty and students is overwhelmingly positive.