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 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) algorithms have been deployed in several areas including energy, imaging, audio processing and other applications. At Arizona State University, the Sensor Signal and Information Processing (SenSIP) center and industry consortium has launched several research projects involving QML especially in the areas of solar energy monitoring and image processing. In addition, SenSIP hosted five National Science Foundation (NSF) workforce development programs that focused on providing ML research experiences to students and community college faculty. More recently the SenSIP center, started research and workforce development projects in QML. Most of these efforts have been supported by NSF and industry. In 2024, these efforts expanded to international engagement with activities at the SS, Cyril and Methodius University (UKIM) with Electrical Engineering and Computer Science students. QML research projects were launched in solar energy fault detection using transformers and imaging studies on edge detection and object recognition. The paper describes our research training efforts in the spring of 2025 where ASU instructors trained graduate students in QML simulations for energy and imaging applications.
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.
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.
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.
Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research.