Introduction

We are a newly established research group in the School of Computer Science, College of Computational, Mathematical, and Physical Sciences (CCMPS), University of Guelph. We advance hybrid quantum–classical machine learning (QML) with a focus on robust training, noise-aware evaluation, and reproducible research software—so that near-term quantum hardware can be assessed credibly and compared fairly across platforms

Research focus: Robust Hybrid Quantum Machine Learning (QML)

Hybrid QML offers a practical path to extracting value from noisy intermediate-scale quantum (NISQ) devices, but progress is limited by unstable optimization, sensitivity to device noise and sampling error, and weak reproducibility across hardware and simulators.  

Our lab addresses these bottlenecks by developing hybrid QML algorithms and benchmarking infrastructure that enable consistent, transparent evaluation.

International collaboration (Canada–Netherlands)

We are establishing a focused collaboration between the University of Guelph and Leiden University to co-design robust hybrid QML methods and to build shared capacity in open-source quantum software, benchmarking, and HQP training.

What we aim to deliver

• Robust hybrid QML algorithms with reference implementations suitable for NISQ-era evaluation.  
• An open, reproducible benchmarking workflow (tasks, evaluation scripts, and reporting templates) to compare results across simulators and available hardware backends.  
• A structured training program for highly qualified personnel (HQP) through co-supervised projects, joint seminars/reading groups, and software development sprints.    
• An enduring Canada–Europe research partnership.

Training and lab culture

We welcome undergraduate, MSc, PhD, and postdoctoral researchers interested in the science and engineering of quantum computation at the intersection of quantum algorithms, optimization under noise, benchmarking, and reproducible research software (version control, testing, documentation, and release workflows)

Principal Investigator:  Dr. Ed Sykes
School of Computer Science 
College of Computational, Mathematical, and Physical Sciences (CCMPS)
University of Guelph 

Visit our associated Research Lab: the AI Lab