October 9, 2025 | Virtual Event: NISS-CANSSI Collaborative Data Science: Working with Physicists on Quantum ML | National Institute of Statistical Sciences
The National Institute of Statistical Sciences (NISS), in collaboration with the Canadian Statistical Sciences Institute (CANSSI), convened researchers from statistics, physics, and data science on October 9, 2025, for a timely webinar exploring the rapidly evolving field of Quantum Machine Learning (QML). The event, part of the ongoing NISS–CANSSI Collaborative Data Science webinar series, drew a broad interdisciplinary audience eager to understand how quantum physics is beginning to reshape data-driven discovery.
Titled “Working with Physicists on Quantum ML,” the one-hour virtual session highlighted both the promise and the challenges of integrating quantum principles with modern machine learning methodologies. Hosted via Zoom, the free webinar underscored NISS and CANSSI’s shared mission to foster cross-domain collaboration at the frontiers of science and data analytics.
Bridging Quantum Physics and Data Science
Quantum Machine Learning sits at the intersection of quantum computing, statistical inference, and algorithmic learning. During the webinar, speakers emphasized that while quantum hardware is still in a developmental phase, conceptual advances in QML are already influencing how researchers think about optimization, pattern recognition, and computational complexity. The session aimed to demystify these ideas for statisticians and data scientists who may not have formal training in physics.
Participants were introduced to the foundational principles of quantum mechanics—such as superposition and entanglement—and how these concepts could enable new forms of learning algorithms. The discussion also addressed the statistical implications of quantum data structures, including uncertainty quantification and reproducibility, which remain central concerns for data science as a discipline.
Expert Perspectives from Statistics and Quantum Research
The webinar featured two distinguished speakers whose careers span mathematics, statistics, and quantum research. Dr. Martin T. Wells, Charles A. Alexander Professor of Statistical Sciences at Cornell University, provided a statistical perspective on QML, emphasizing the importance of rigorous inference and interpretability as quantum-enhanced methods emerge. Dr. Wells drew on decades of experience in applied and theoretical statistics, highlighting parallels between earlier computational revolutions and today’s quantum moment.
Joining him was Dr. Luca Candelori, mathematician and Director of Research at Qognitive, Inc., who offered insights from the physics and mathematics side of quantum technologies. Dr. Candelori discussed how modern mathematical tools—particularly those related to geometry and algebra—are being used to characterize quantum systems and inform machine learning models. His remarks illustrated how close collaboration between physicists and data scientists is essential for translating theoretical advances into usable algorithms.
The session was moderated by Dr. Emily Casleton, a statistician with the Statistical Sciences Group at Los Alamos National Laboratory. As moderator, Dr. Casleton guided the discussion toward practical collaboration strategies, encouraging participants to think critically about how interdisciplinary teams can overcome differences in language, methodology, and research culture.
Emphasizing Collaboration as a Scientific Imperative
A central theme of the webinar was that progress in Quantum Machine Learning will depend as much on collaboration as on computation. Speakers noted that statisticians bring essential expertise in model validation and uncertainty assessment, while physicists contribute deep understanding of quantum systems and hardware constraints. Bridging these perspectives, they argued, is necessary to ensure that QML developments are both scientifically sound and practically relevant.
This emphasis aligns with the broader goals of the NISS–CANSSI Collaborative Data Science initiative, which seeks to promote interdisciplinary research, reproducibility, and science-led innovation. By bringing together domain scientists and data scientists, the initiative aims to accelerate discovery while maintaining rigorous standards of statistical practice.
Looking Ahead
Although the event marked a snapshot of an emerging field, speakers and attendees alike acknowledged that Quantum Machine Learning remains a long-term endeavor. Near-term impacts are likely to be felt in hybrid classical–quantum approaches, as well as in new theoretical frameworks inspired by quantum thinking. The webinar concluded with a forward-looking discussion about training the next generation of researchers to work fluently across statistics, physics, and data science.
As part of the continuing NISS–CANSSI webinar series, “Working with Physicists on Quantum ML” reinforced the importance of interdisciplinary dialogue at a time when scientific challenges increasingly demand collaborative solutions. Recordings and follow-up materials from the event are expected to further extend its impact within the research community.