Advancing research, education, and collaboration for quantum and classical machine learning.
Who We Are
The UofT Machine Intelligence Club is a student-led research organization at the University of Toronto. We bring together undergraduates, graduate students, and faculty to study the mathematical foundations, algorithmic advances, and near-term applications of quantum and classical machine learning.
Every research thread begins with mathematical clarity. We prioritize understanding over intuition and proof over conjecture.
Our reading groups, workshops, and projects track the fastest-moving areas in ML and quantum computing, from barren plateaus to dequantization.
Senior members mentor juniors. Across departments — CS, Physics, ECE, Mathematics — we share knowledge, code, and curiosity.
Research
From learning theory to quantum hardware experiments, our work spans the full depth of modern machine intelligence.
Our ML Theory stream provides the mathematical foundations that underpin both classical and quantum learning. We study the statistical limits of learning algorithms, the geometry o…
Quantum Machine Learning (QML) investigates how quantum mechanical phenomena — superposition, entanglement, and interference — can be leveraged to learn from data more efficiently…
In the NISQ era, neither fully classical nor fully quantum approaches are likely to be optimal across all tasks. Our Hybrid Systems stream explores architectures and algorithms tha…
The Theory Reading Group meets weekly to work through foundational and frontier papers in quantum information, computational complexity, and statistical learning. Attendance is ope…
Publications
Technical reports, workshop papers, and educational materials produced by MIC members.
Mei-Ling Chen, Tobias Richter, Priya Nair
Quantum PCA offers theoretical speedups over classical algorithms for dimensionality reduction in high-dimensional settings. This survey synthesizes recent developments in quantum PCA variants, includ…
Priya Nair, Aiden Park, Sofia Martinez
We investigate generalization properties of variational quantum circuits (VQCs) used as machine learning models. Drawing on statistical learning theory, we derive uniform convergence bounds for VQC fu…
Sofia Martinez, James Okonkwo, Mei-Ling Chen
Scientific ML tasks often involve structured data with physical symmetries that quantum systems can naturally encode. We present a framework for hybrid quantum-classical pipelines that leverage quantu…
Events
Workshops, guest lectures, reading groups, and hack sessions open to all members.
This session continues our series on quantum advantage in machine learning. We will closely examine Tang's dequantization results for quantum-inspired recommendation systems and their implications for the quantum PCA speedup claim. Members are expected to have…
Dr. Chiara Bonfiglioli — Perimeter Institute for Theoretical Physics
We are pleased to welcome Dr. Chiara Bonfiglioli (Perimeter Institute for Theoretical Physics) for a public lecture on the current state of quantum advantage claims in the NISQ era. Dr. Bonfiglioli will review recent supremacy experiments, discuss noise thresh…
A practical three-hour workshop for members with little to no quantum programming experience. Participants will install and configure Qiskit, build their first quantum circuits, implement a variational quantum eigensolver (VQE) from scratch, and run experiment…
Join a community of students and researchers pushing the frontier of quantum and classical machine learning at the University of Toronto.