Large scale simulations of photonic quantum computers
Current quantum computers are in the “NISQ”, or Noisy-Intermediate-Scale-Quantum regime. The true potential of quantum computing will only be realized when noise levels are reduced or controlled, and large scale is achieved. Xanadu’s approach is to use photonic technology as the building blocks of their machines. This project addresses the question: How do we build a useful quantum computer based on imperfect photonics components? There are 2 specific aspects of this general question: A – In which conditions does a photonic quantum computer reach quantum advantage (demonstrating large speedups compared to today’s most powerful conventional computers)? B – What are the resources required to build a scalable fault-tolerant photonic quantum computer? This project will provide the team with a much more detailed understanding of the requirements and tradeoffs involved in the future, much larger-scale generations of quantum photonic hardware that must be built in order to fully realize the theoretical potential of quantum computing.By mapping out a pathway to demonstrating quantum advantage, and large-scale, fault-tolerant quantum computing, this project will guide Xanadu’s ongoing efforts to build more powerful quantum computers which can deliver commercial benefits to customers and ensure economic impact.
Machine Learning Methods for Behavioural Biometrics
The project will focus on creating a Data architecture that will be composed of models, policies, rules and standards that will monitor which data is collected, and how it is stored, arranged and integrated into the system. The task of the professor and the student will be to create a data architecture and a machine learning algorithm that will help build a robust user-profile system that will help extract, store, build and analyse up to 1 million user profiles. This profile system will also be in charge of generating at least 50 behavioral data or 64 bytes of data per second. Simultaneously, the given data architecture will provide over 5 million user-profile recognitions per day through the use of predictive modeling and the given REST API call. This will help detect suspicious activities and anomalies without the use of browser cookies, location, and hardware information. For this project, the professor and the student will be given access to a part of F8th’s repository and must work together as a team to build a user-profile system that will help extract the given user profiles. This data architecture will help tolerate the behavioral data noise caused by the modification of input devices such as a mouse, keyboard, mobile and laptop. As F8th IDaaS is an Identity as a Service Solution (as described above), it is necessary to provide predictions on the given trained models (also known as behavioral biometrics). The market requires the predictions of the models to be done in less than 1 second (something that will be discussed later on when the project is being worked on by the professor and the student). The service must be provided at a reasonable cost and time; hence, it is very important to monitor the data as well as the resource consumption continuously. Lastly, as the project is to be worked on between the professor and the student as a team-constant discussions, comparisons, and decisions regarding the quality and the affordability of the solution will have to be done by the student and the professor.
Smart Handling of Big Radio Signal Data
This project will take advantage of some of the world’s largest data sets produced by radio astronomy, established University of Toronto and Ontario astronomy expertise, and technology from IBM to strengthen IBM Canada’s position in the upcoming generation of big projects including signal processing for ALMA (Atacama Large Millimeter/submillimeter Array) and SKA (Square Kilometer Array).