CDL-Toronto is located at the Rotman School of Management at the University of Toronto. Launched in 2012, it was the first CDL location established and was the brainchild created by the need to merge science-based projects with business expertise to create massively scalable ventures. It currently offers three streams: Prime, Artificial Intelligence, and Quantum Machine Learning. The QML stream is the latest initiative, varying from CDL-Toronto’s two other streams in that it offers accepted applicants access to capital, working space, and quantum computing resources from technology partners D-Wave Systems and Rigetti.  


Situated in the Toronto-Waterloo tech corridor, CDL-Toronto provides startups with access to business development support from top business students at the Rotman School of Management, funding from leading VC firms, and resources to scale.


The general technology or “Prime” stream includes startups in an array of industries with deep-science and technology innovations. The Prime stream is industry agnostic, with startups tackling problems in healthcare, finance, energy, chemical, media, transportation, and agriculture sectors, amongst others. Technologies in the Prime stream include advanced engineering, electronics, blockchain, nanotech, neuroscience, and general IT applications.

Artificial Intelligence/Machine Learning

CDL-Toronto’s AI stream was launched in 2015 and was the first program in the world to focus exclusively on massively scalable artificial intelligence and machine learning-driven startups. CDL-Toronto’s AI/ML stream connects startups to the University of Toronto, University of Waterloo and the newly formed Vector Institute for Artificial Intelligence founded by Geoffrey Hinton, the “Godfather of AI.”

Quantum Machine Learning

The QML stream is the first of its kind and provides applicants with access to technical training, capital, and quantum computing resources from Rigetti and D-Wave Systems. Technical education will also be provided by Dr. Peter Wittek, author of the first textbook on quantum machine learning. Individuals/teams with a strong background in physics, math, statistics, machine learning, and/or electrical engineering and a keen interest in quantum computing are encouraged to apply.

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