Quantum machine learning (QML)—a field often associated with complexity—is about to become far more approachable, thanks to Kennesaw State University (KSU) researchers.
Funded by the National Science Foundation (NSF), the transformative new initiative is paving the way for accessible quantum learning. By creating a browser-based, portable system that seamlessly integrates into existing courses, the project eliminates technical barriers—no prior experience or software installation required. With intuitive tools and hands-on resources, students can explore quantum data processing with greater ease.
At a time when data doubles every two years and is expected to exceed 40 billion gigabytes in 2025, the need for innovative solutions is pressing. Quantum superpositions and entanglements could significantly enhance traditional machine learning by boosting speed and accuracy, especially for large-scale training data. Yong Shi, an associate professor at KSU and an expert in quantum machine learning (QML), is leading the charge to address this challenge.
Shi, along with Professor Dan Lo and Assistant Professor Luisa Nino, recently secured an NSF grant to develop open-source, hands-on QML training materials to address the shortage of researchers and their limited presence in higher education.
Quantum machine learning is a discipline that combines the advanced abilities of quantum computers with techniques that help machines learn from large data sources. Unlike regular computers that handle information step-by-step, quantum computers use the principles of quantum mechanics to process large amounts of data simultaneously. This allows them to find patterns that traditional computers might miss.
In collaboration with Florida A&M University, Kennesaw State will create nine training modules with hands-on labs covering key quantum computing concepts and their applications in computer science and industrial engineering. These modules will be integrated into existing courses, accompanied by faculty workshops and student training camps, ultimately enhancing research and creating diverse communities in QML.
“This initiative is not just about teaching QML; it’s about building a supportive ecosystem that empowers both students and faculty to innovate and collaborate,” said Shi, who teaches in KSU’s College of Computing and Software Engineering.
During his time as a Ph.D. student, Shi began recognizing gaps between traditional data analysis methods and the increasing volumes of data generated daily. His passion stems from a desire to equip students and faculty across various fields, particularly computer science and industrial engineering, with cutting-edge tools that can significantly improve decision-making and research outcomes.
A significant hurdle in advancing QML is the shortage of skilled researchers. Shi noted that many universities are not yet prepared to teach QML effectively.
“Our goal is to develop a cloud-based lab environment where students can learn and apply QML techniques without needing extensive installations or prior experience,” Shi said.
Through this initiative, KSU aims to create a portable system that allows universities to integrate QML modules into their existing courses. By providing flexible resources, the project aims to make it easier for schools to adopt QML training that fits their specific needs.
The training materials will offer several unique features. Everything runs in a browser, so users can dive in right away without software. Plus, the modules are built to be interdisciplinary, bringing together students from computer science and industrial engineering to collaborate and innovate.
Part of Shi’s vision includes fostering a robust research community. Collaborating with institutions like Florida A&M is a cornerstone of this initiative.
As the project progresses, Shi and his research team are focused on evaluating the effectiveness of their modules. They will implement surveys before and after courses to gather feedback, which will help refine their approach and ensure that students gain valuable insights into QML.
Optimistic about the potential of QML, Shi sees it revolutionizing research across a wide range of scientific disciplines. From healthcare to environmental science, quantum machine learning could open up new possibilities for data analysis and decision-making.
Sumanth Yenduri, dean of the College of Computing and Software Engineering, credits Shi for his research and hard work.
“Shi’s research not only advances the field of quantum machine learning but also sets a new standard for interdisciplinary collaboration and innovation in education,” Yenduri said.
With this initiative, KSU intends to prepare students and faculty alike to thrive in both a rapidly evolving field and digital world. By breaking down barriers to quantum learning, KSU can provide a practical advantage that applies across many fields, empowering more students and professionals to lead with data and solve real-world problems.
