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.
Betakit | October 25, 2017
How quantum machine learning will solve problems once thought out of reach
Dimple | October 20, 2017
Toronto’s Creative Destruction Lab is training a generation of quantum computing experts
Nature | October 19, 2017
Quantum machine goes in search of the Higgs boson
UofT News | October 02, 2017
A quantum leap? Inside a U of T accelerator’s bold bet on the future of artificial intelligence
The Globe and Mail | September 27, 2017
Inside the race to produce the world’s first quantum computer
Nature | September 14, 2017
Quantum Machine Learning – Review article co-authored by Dr. Peter Wittek
Nature | September 13, 2017
First quantum computers need smart software
Click here to read more about CDL’s Quantum Machine Learning program in the news.
• 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 – Quantum computing resources from our technology partners:
• 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. (Click here to read Dr. Wittek’s latest co-authored review article in Nature).
• Technical Support – Multi-day training, hands-on troubleshooting, and ideation with an on-site D-Wave QML expert. Quantum computing hardware and software development training by Rigetti.
• 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 – Technical Co-Founders
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.
Who Should Apply – Business Co-Founders
We will also be admitting motivated, ambitious, and curious individuals with business backgrounds to pair with technical participants as co-founders of QML startups. Preference will be given to applicants who are graduates of the Rotman School and/or who have previous experience raising capital for startups.
Applications will be considered in rounds, with applications closing at 11:59pm EST on the following dates:
*Applications are now closed for the 2017-2018 cycle. If you are based in Canada and would like to be considered, please email firstname.lastname@example.org before filling out the application.
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.
Who are the technology partners?
D-Wave Systems – www.dwavesys.com
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. Our mission is to unlock the power of quantum computing to solve the world’s most challenging problems. Our 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 over 140 U.S. patents and has published over 90 peer-reviewed papers in leading scientific journals.
D-Wave is a privately held company, with offices in Vancouver, British Columbia; Palo Alto, California; and Hanover, Maryland.
Rigetti – www.rigetti.com
Rigetti Computing is a full-stack quantum computing company. We design and manufacture superconducting quantum integrated circuits. We package and deploy those chips in a low temperature environment, and we build control systems to perform quantum logic operations on them. We build software to integrate our systems directly into existing cloud infrastructure.
We believe the first useful quantum computers are within reach, and we believe quantum computing has the potential to one day have an enormous positive impact on humanity. To help realize that vision, we also develop new algorithms for quantum computing, with a focus on near-term applications in computational chemistry and machine learning. Our product, Forest, is the world’s first full-stack programming and execution environment for quantum/classical computing. Forest includes Quil (quantum instruction language), our programming standard for quantum/classical computing.
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