The Quantum Machine Learning Incubator Stream at CDL-Toronto 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.
“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.”
Founder & CEO, Xanadu
Ideal QML Incubator Stream candidates are creative, intelligent, and ambitious individuals with technical competency in machine learning, computational science, physics, math, statistics, or engineering; business acumen and experience in technology startups; or relevant industry experience or expertise. The Incubator Stream’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:
The list above is not exhaustive.
Contact email@example.com to discuss the Incubator Stream with a QML staff member.
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 10-month duration.
Full list of CDL-Toronto Fellows and Associates
Teams accepted to the CDL QML Incubator Stream 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.
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.
The QML Incubator Stream operates one annual cohort at CDL-Toronto. Participating founders are strongly encouraged to relocate to Toronto, Canada in order to take advantage of the Incubator Stream features. For more information or to schedule an introduction meeting with the CDL team, email firstname.lastname@example.org
By 2022, the QML Incubator Stream 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.
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.
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)
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. Python and Tensorflow experience is required.
Participants must be in Toronto for the technical bootcamp of the Incubator Stream during the month of July 2019 and for group sessions with CDL mentors in October 2019, December 2019, February 2020, April 2020, and June 2020. 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.
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 2019, and the second $40k after companies successfully pass the fourth Q7 session in April 2020. Companies that do not pass the fourth session will receive $40k for 4% equity.
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.
Yes, we will accept individuals with strong technical backgrounds. Submit an application, and a CDL team member will contact you.
Yes. Depending on your nationality, you may need a visa. If you are accepted, we can provide you access to support from a Canadian immigration law firm, which can help you obtain the necessary entry permits.
Yes, particularly if they have specific industry expertise. The application is the same for those with technical and business backgrounds.