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.
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
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
Click here to read more about CDL’s Quantum Machine Learning program in the news.
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.
The program will be located at the Rotman School of Management at the University of Toronto in downtown Toronto, Ontario, Canada.
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.
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)
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.
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.
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.
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.
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.
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. 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.
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.
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.