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The Quantum Machine Learning Incubator Stream brings together entrepreneurs, investors, AI experts, leading quantum information researchers, and quantum hardware companies (D-Wave Systems, Rigetti Computing, and Xanadu) to build ventures in the nascent domain of quantum machine learning and quantum optimization.

Participants also receive US$80k equity investment from three prominent venture capital firms, office space in downtown Toronto, and intensive technical training from industry and academic leaders in quantum computing and machine learning.

CDLQMLIncubatorStream

Full list of CDL-Toronto Scientists   The QML Incubator Stream offers a robust set of resources for new founders to launch and scale a startup.

  Quantum computers make direct use of the odd characteristics of quantum physics, such as superposition (a quantum bit (qubit) having multiple values at the same time) and entanglement (two qubits sharing and communicating certain characteristics despite large distances).

tunneling), quantum computers have a high degree of control over quantum states, as well as a mechanism to prevent the decoherence of the quantum states on reasonable time scales.

This may involve performing classical computation on data from quantum sensors or using a quantum computer to enhance machine learning on classical data.

While scalable universal quantum computers are still a long way off, quantum machine learning may benefit from using current and near future quantum information processing devices.

Further background information on QML can be found through the following articles: “Quantum Machine Learning” (technical) “Quantum Machine Learning: Path to a Better Artificial Intelligence?” (nontechnical) For applicants coming from a computer science background, having a solid understanding of machine learning, probabilistic graphical models, statistics, and Monte Carlo methods is recommended, along with experience with distributed systems.

For physicists, quantum computing, quantum many-body systems, and quantum information processing are the most relevant areas, and experience with large-scale numerical computations is a great advantage.

Participants are strongly encouraged to live in Toronto for the rest of the program to best make use of CDL resources, to work through problems with the technical team, to have rich interactions with the city’s AI ecosystem and because Toronto is a fantastic place to build a tech company.

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