By 2022 the QML Initiative 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.
• Business Guidance and Investor Access – Objective-setting meetings with the CDL Fellows, 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, each with a significant portfolio of machine learning oriented investments: Bloomberg Beta, Data Collective (DCVC), and Spectrum 28
• Quantum Access – Cloud access to the D-Wave Sampling Service, which provides an interface to the D-Wave2000Q quantum computer and the D-Wave sampling servers
• Technical Education – One month of intensive technical training and weekly office hours thereafter led by Peter Wittek, author of the first textbook on quantum machine learning, with tutorials by other experts
• Technical Support – Hands-on troubleshooting and ideation with an on-site D-Wave QML expert
• Business Support – Business development support from MBAs at Canada’s top-ranked business school, the Rotman School of Management at the University of Toronto
Who Should Apply
Creative, intelligent, and ambitious individuals and teams with a strong background in machine learning, physics, math, statistics or electrical engineering with a keen interest in quantum computing. Our focus is on early-stage companies (pre-seed) or even projects (pre-incorporation) however, startups at all levels of maturity will be considered. All Lab programming and training will be conducted in English.
Applications will be considered in rounds, with applications closing at 11:59pm EST on the following dates:
Applicants from outside Canada are strongly encouraged to apply in an earlier round to leave ample time to sort out relocation and immigration logistics.
The program will be located at the Rotman School of Management at the University of Toronto in downtown Toronto, Ontario, Canada.
What is quantum computing?
Quantum computers make direct use of the odd characteristics of quantum physics, such as superposition and entanglement. The word “direct” is important, since ordinary semiconductor-based computers also use quantum effects, such as tunnelling, whereas quantum computers must 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 the performance of learning algorithms. 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 (e.g., quantum annealers and integrated photonic circuits). Just as quantum key distribution systems and quantum random number generators are already commercially available, quantum machine learning has the potential to be a meaningful industrial application of quantum information technologies.
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?” (Non-technical)
What kinds of problems lend themselves most to quantum machine learning?
Quantum mechanics is quintessentially probabilistic, so it is not surprising that probabilistic machine learning models, such as Boltzmann machines, fare the best. Given that scaling up a quantum technology is a challenging task, it is also better to focus on models in which computations dominate as opposed to the sheer number of parameters.
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 training portion of the program during the month of September 2017 and for group sessions with CDL mentors in October 2017, December 2017, February 2018, April 2018, and June 2018. You are strongly encouraged to stay 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 they opt in or out of the pre-seed investment at the outset of the program. Those that opt in receive US$80k for 8% equity. The $80k investment is split into two tranches. The first $40k is distributed in late October 2017, after companies have incorporated and presented their initial plan to the Q7, the second $40k after companies successfully pass the fourth Q7 session in April 2018. Companies that do not pass the fourth session only receive $40k for 4% equity.
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.
How can entrepreneurs without physics or ML experience still be involved?
In Summer 2017, we will open a separate application for individuals with other talents (e.g., business development) to join and work with technical founders already admitted to the program.
Is this program for all ML/AI companies?
No. This program will focus on machine learning companies that are specifically interested in working on the type of problems that will benefit from a quantum lift. We have an ML/AI stream for which we are welcoming applications now