For founders pursuing commercial opportunities at the intersection of quantum computing and machine learning.

The Quantum Machine Learning Program at CDL-Toronto is an incubator where founders build startups at the intersection of quantum computing and machine learning.

The program brings together entrepreneurs, investors, AI experts, leading quantum information researchers, and our technology partners, D-Wave Systems, Rigetti Computing, and Xanadu to create new ventures at the frontier of quantum machine learning. Participants also receive US$80k equity investment from Bloomberg Beta, Data Collective, and Spectrum28, office space in downtown Toronto, and intensive technical training from industry and academic leaders in quantum computing and machine learning.

Startups must attend a one-month technical bootcamp in Toronto in July 2018 and five in-person objective-setting sessions in Toronto between October 2018 and June 2019. Learn more about the CDL program

“As someone who walks the line between academia and entrepreneurship, the Lab was very useful as a means to keep Xanadu on track, achieving necessary milestones we needed to derisk the company for our first round. For that I am truly indebted…Overall, the Lab has been fundamental to Xanadu’s success.”

Christian Weedbrook

Founder & CEO, Xanadu

Who Should Apply?

Ideal QML Program candidates are creative, intelligent, and ambitious individuals and have strong backgrounds in machine learning, physics, math, statistics, or electrical engineering alongside a keen interest in quantum computing. The program’s focus is on early-stage companies (pre-seed) or even projects (pre-incorporation); however, startups at all levels of maturity will be considered. All QML programming and training is conducted in English and takes place in Toronto, Canada.

Example Innovation Areas:

  • materials
  • natural language processing
  • optimization

The list above is not exhaustive.

Contact to discuss the program and your venture with someone from the QML Program.

Representative CDL Alumni

Our Mentors

CDL mentors include accomplished entrepreneurs, experienced operators, and active angel and venture investors. Mentors meet every eight weeks in Toronto to help founders set objectives over the program’s nine-month duration.

Geordie Rose
Sally Daub
James Hardiman
Andrew Fursman

Full list of CDL-Toronto Fellows and Associates  

Our Scientists

Teams accepted to the CDL QML Program benefit from extensive advising sessions and regular office hours with quantum physics, machine learning, and quantum computing experts. These scientists help to evaluate approaches to building technology, suggest improvements, and explore formal advising engagements.

Peter Wittek
Seth Lloyd
Roger Melko
Michele Mosca

Full list of CDL-Toronto Scientists  

Program Features

The QML Program offers a robust set of resources for new founders to launch and scale a startup.

  • Business Guidance and Investor Access – Objective-setting meetings with the CDL Fellows and Associates, a group of highly successful entrepreneurs and investors with expertise in scaling high-tech startups.
  • Capital – Pre-seed investment from three Silicon Valley-based venture capital firms: Bloomberg Beta, Data Collective (DCVC), and Spectrum 28.
  • Quantum Access – Quantum computing resources from our technology partners:
    • D-Wave – D-Wave Sampling Service, an interface to the D-Wave2000Q quantum computer and the D-Wave sampling servers
    • Rigetti Computing – Rigetti Forest programming environment, with access to cloud-connected superconducting quantum processors and Quantum Virtual Machine (QVM);
    • Xanadu – Strawberry Fields, an open-source quantum software library with a Python interface and a suite of simulators for execution on CPU/GPU; an industry-first machine learning toolbox for quantum computing (powered by TensorFlow).
  • Technical Education – QML bootcamp lead by Professor Peter Wittek, author of the first textbook on quantum machine learning, with tutorials from other experts. Click here to read Dr. Wittek’s latest coauthored review article on QML in Nature.
  • Business Support – Business development support from MBAs at Canada’s top business school, the Rotman School of Management at the University of Toronto.


The QML Program operates one annual cohort at CDL-Toronto. Participating founders are strongly encouraged to relocate to Toronto, Canada in order to take advantage of the program features.   For more information or to schedule an introduction meeting with the CDL team, email  

Applications for the Quantum Machine Learning program have now closed.



By 2022, the QML Program will have produced more well- capitalized, revenue generating quantum machine learning software companies than the rest of the world combined. The majority of these will be based in Canada.  

Common Questions

What is quantum computing?

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). While classical computation can make use of quantum effects (e.g. 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.

What is quantum machine learning? How is it different than what exists now?

Quantum machine learning uses quantum technologies to improve the speed and performance of learning algorithms. 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)  

What are the skills and knowledge prerequisites for the program?

For applicants coming from a computer science background, having a solid understanding of machine learning, probabilistic graphical models, statistics, and Monte Carlo methods is essential, 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. Python and Tensorflow experience is required.

Do participants need to be in Toronto for the duration of the program?

Participants must be in Toronto for the technical bootcamp of the program during the month of July 2018 and for group sessions with CDL mentors in October 2018, December 2018, February 2019, April 2019, and June 2019. 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.

How does the investment work?

Companies must decide whether to opt in or out of the pre-seed investment at the outset of the program.  Those that opt in and obtain approval for their business proposals will receive US$80k for 8% equity, split into two tranches. The first $40k will be distributed in September 2018, and the second $40k after companies successfully pass the fourth Q7 session in April 2019. Companies that do not pass the fourth session will receive $40k for 4% equity.

Who are the technology partners?

D-Wave Systems Founded in 1999, D-Wave Systems is the world’s first quantum computing company and the leader in the development and delivery of quantum computing systems and software. Its mission is to unlock the power of quantum computing to solve the world’s most challenging problems. D-Wave systems are being used by world-class organizations and institutions including Lockheed Martin, Google, NASA, USC, USRA and Los Alamos National Laboratory. D-Wave has been granted more than 140 US patents and has published over 90 peer-reviewed papers in leading scientific journals.   Rigetti Rigetti Computing is a full-stack quantum computing company that designs and manufactures superconducting quantum-integrated circuits. Rigetti packages and deploys those chips in a low-temperature environment, and builds control systems to perform quantum logic operations on them. Rigetti believes that the first useful quantum computers are within reach and that quantum computing has the potential to one day have an enormous positive impact on humanity. To help realize that vision, it is also developing new algorithms for quantum computing, with a focus on near-term applications in computational chemistry and machine learning. Forest is the world’s first full-stack programming and execution environment for quantum/classical computing and includes Quil (quantum instruction language), the Rigetti programming standard for quantum/classical computing.   Xanadu Xanadu is a light-based quantum computing company that creates silicon photonic chips to build a truly full-stack solution. Instead of using electrons to carry information and perform calculations, Xanadu uses photons. Unlike electrons, photons are very stable and are almost unaffected by random noise from heat. We use photonic chips to generate, control, and measure photons in ways that enable extremely fast computation. Strawberry Fields, Xanadu’s programming platform, is a library for simulation, optimization and quantum machine learning of continuous-variable circuits. The platform contains a Python API for quantum programming based on our user-friendly Blackbird language and a suite of virtual quantum computer backends built in NumPy and TensorFlow.

I have a background in machine learning or physics but don’t have a startup or team. Can you introduce me to others in the program?

Yes, we will accept individuals with strong technical backgrounds. Submit an application, and a CDL team member will contact you.

I’m not Canadian. Can I still apply?

Yes. Depending on your nationality, you may need a visa. Once you are accepted, we will work with applicants to ensure they will be able to enter and build businesses in Canada. International candidates are strongly encouraged to apply early.

Can entrepreneurs without physics or ML experience still be involved?

Yes, particularly if they have specific industry expertise.  The application is the same for those with technical and business backgrounds.

QML in the News

Ontario (Gov) | February 23, 2018 World’s first Quantum Machine Learning startup program lives in Ontario   Betakit | October 25, 2017 How quantum machine learning will solve problems once thought out of reach   Click here to read more about CDL’s Quantum Machine Learning program in the news.