Navigating Quantum and AI Career Trajectories: A Beginner’s Mini-Course on Computational Methods and their Applications

21 May 2024, 08:00 → 24 May 2024, 17:00 America/Toronto

PI/2-292 - Time Room (Perimeter Institute for Theoretical Physics)

Mohamed Hibat-Allah (Perimeter Institute for Theoretical Physics)

The dynamic field of quantum physics and artificial intelligence is expanding across both academic and industrial landscapes. This mini-course offers an introduction to computational techniques currently utilized in the quantum sector, highlighting non-academic career paths for individuals interested in quantum physics and machine learning. The program features two lecture series: one on generative modeling - covering topics (such as restricted Boltzmann machines, recurrent neural networks, and transformers) - and the other on quantum machine learning algorithms. Participants will also benefit from practical coding tutorials, networking opportunities, and related events about the landscape of Quantum and AI.

 

Land Acknowledgement

In the spirit of understanding and learning from what has come before, Perimeter Institute respectfully acknowledges that we are located on the traditional territory of the Attawandaron, Anishnaabeg, and Haudenosaunee peoples.

Perimeter is situated on the Haldimand Tract, land promised to Six Nations, which includes six miles on each side of the Grand River. As settlers, we thank all the generations of people who have taken care of this land for thousands of years. We are connected to our collective commitment to make the promise and the challenge of Truth and Reconciliation real in our communities.

 

 

Tuesday, 21 May

08:30 Registration

1 Opening Remarks

Lecture: Quantum Machine Learning

Lectures

Convener: Alvaro Ballon

2 Quantum Machine Learning

Speaker: Alvaro Ballon Bordo (Xanadu)

11:00 Break

Lecture: Restricted Boltzmann Machines (RBMs)

Lectures

Convener: Mohamed Hibat-Allah

3 Restricted Boltzmann Machines (RBMs)

Speaker: Mohamed Hibat-Allah (Perimeter Institute for Theoretical Physics)

13:00 Lunch and free time (Lunch is NOT provided)

4 Quantum and AI ethics session - hosted by Quantum Ethics Project

15:00 Break

15:30 Tutorial (Quantum Machine Learning)

Wednesday, 22 May

Lecture: Quantum Machine Learning

Lectures

Convener: Alvaro Ballon

5 Quantum Machine Learning

Speaker: Alvaro Ballon Bordo (Xanadu)

11:00 Break

11:30 Tutorial (RBMs)

13:00 Lunch and free time (Lunch is NOT provided)

6 Colloquium: Deeptech Commercialization through Entrepreneurial Capabilities

Deeptech or science-based innovations often spend more than a decade percolating within academic and government labs before their value is recognized (Park et al., 2022). This development lag time prior to venture formation is only partly due to technological development hurdles. Because science-based inventions are often generic in nature (Maine & Garnsey, 2006), meaning that they have broad applicability across many different markets, the problem of identifying a first application requires the confluence of deep technical understanding with expert knowledge of the practice of commercialization. This process of technology-market matching is a critical aspect of the translation of science-based research out of the lab (Pokrajak 2021, Gruber and Tal, 2017; Thomas et al, 2020, Maine et al, 2015) and is often delayed by a lack of capacity to identify, prioritize and protect market opportunities.

Typically, deeptech innovations can take 10-15 years of development, and tens (or even hundreds) of millions of dollars of investment to de-risk before a first commercial application (Maine & Seegopaul, 2016). Academics seeking to commercialize such inventions face the daunting challenge of competing for investment dollars in markets that are ill suited to the uncertainty and timescales of deep tech development. The time-money uncertainty challenge faced by science-based innovators is compounded by the fact that most of the scientists and engineers with the world-leading technical skills required to develop science-based inventions, lack innovation skills training, and so cannot navigate the complexities of early and pre-commercialization development critical to venture success.

Some researchers, having developed a mix of technical and business expertise, have demonstrated a long-term ability to serially spin out successful ventures (Thomas et al., 2020). Entrepreneurial capabilities, which can be learned, enable scientist-entrepreneurs to play formative roles in commercialising lab-based scientific inventions through the formation of well-endowed university spin-offs. (Park et al, 2022; 2024). Commercialization postdocs, when supported by well designed training, stipends, and de-risking supports, can lead the mobilization of fundamental research along multiple commercialization pathways. Recommendations are provided for scholars, practitioners, and policymakers to more effectively commercialise deeptech inventions.

Speaker: Elicia Maine

15:30 Break

7 Panel discussion: Turning Invention into Innovation

Turning Invention into Innovation: Exploring Opportunities in Quantum and AI

Speakers: Elicia Maine, Jake Malliaros, Melissa Chee, Paul Smith, Raymond Laflamme

Thursday, 23 May

Lecture: Quantum Machine Learning

Lectures

Convener: Alvaro Ballon

8 Quantum Machine Learning

Speaker: Alvaro Ballon Bordo (Xanadu)

11:00 Break

Lecture: Recurrent Neural Networks (RNNs)

Lectures

Convener: Schuyler Moss

9 Recurrent Neural Networks (RNNs)

Speaker: Schuyler Moss (University of Waterloo)

13:00 Lunch and free time (Lunch is NOT provided)

14:00 Speed Networking Event

15:00 Break

15:30 Tutorial (Quantum Machine Learning)

Friday, 24 May

Lecture: Transformers

Lectures

Convener: Mohamed Hibat-Allah

10 Transformers

Speaker: Mohamed Hibat-Allah (Perimeter Institute for Theoretical Physics)

11:00 Break

11:30 Tutorial (RNN wave functions)

11 Concluding Remarks