Wednesday, September 23, 2026 - Friday, September 25, 2026 (all day)

ML4QT Symposium | Machine Learning to Advance Quantum Technologies

Fostering exchange and collaboration among experts from academia, research institutions, and industry.

The Machine Learning to Advance Quantum Technologies (ML4QT) Symposium serves as a leading forum for researchers and developers interested in the convergence of machine learning and quantum innovation. Whether you are actively working in this space or seeking to get involved, ML4QT provides an ideal environment to discover new opportunities, foster collaboration, and help drive the next generation of quantum technologies.

This second annual gathering will be held at the Institute for Quantum Computing, within the Quantum-Nano Centre, University of Waterloo from the 23 to 25 of September 2026.

Important Dates

July 1, 2026|Call for Contribution Submission Deadline
July 1, 2026 | General Registration Opens
August 1, 2026 | Contribution Decision Notifications
September 1, 2026 | Registration Closes
September 23 - 25 | ML4QT Symposium

Have questions about the event? Contact us at iqc.events@uwaterloo.ca

Invited Speakers

Barry Sanders, University of Calgary

Artificial Intelligence for Representing and Characterizing Quantum Systems

I review how integrating AI into quantum-system characterisation is typically based on machine learning, deep learning and language models for the core tasks of quantum-property prediction and quantum-system reconstruction with applications to certification, benchmarking, enhancing quantum information processing and identifying critical quantum phenomena. https://arxiv.org/abs/2509.04923

Manas Mukherjee, National University of Singapore

Seeking advantage in Quantum-Classical Machine Learning with trapped ion system

Quantum technologies, specifically Quantum Computing (QC), represent a paradigm shift in computational power. However, the roadmap to utility is hindered by the inherent fragility of quantum states—a hurdle that makes scaling the industry’s greatest challenge.

To address this, we have developed a quantum-classical hybrid framework designed to push the boundaries of Quantum Machine Learning (QML). By leveraging the trapped-ion platform—noted for its superior coherence times and high gate fidelities—we have demonstrated that quantum classifiers can achieve over 98% fidelity in supervised learning tasks using real-world datasets [1, 2].

While achieving parity with classical systems is a significant milestone, our current research pivots toward the ultimate goal: identifying the specific parameters and architectures where QML provides a definitive advantage over classical counterparts. Here, we will discuss detail of our findings across varied application contexts, highlighting the conditions under which quantum enhancement becomes a reality.

[1] 10.1103/physreva.106.012411

[2] 10.1016/j.isci.2025.113058