Grants and Contributions

Develop digital adoption plan

Assess feasibility of two plant-based protein opportunities for Alexander First Nation

Scale up and improve robotic tank cleaning system for industrial storage tanks

Establish a centre of quantum technology expertise in Calgary, Alberta

Develop Indigenous tourism experiences in Calgary

The Project employs modern machine learning to accelerate the development of industry-ready quantum computing technologies. To this end, the Project addresses the key bottleneck in quantum computing technologies, quantum errors, by pioneering advancements in quantum error prevention and correction through machine learning methods. Machine learning methods, and in particular transformers, possess the potential to learn and model the patterns and dynamics of quantum errors in individual quantum processor hardware in unprecedented detail and with unprecedented adaptability to individual quantum processors. This opens the prospect of machine-learned quantum error handling strategies that are significantly more complex, and efficient, than the more rigid manually developed methods currently in use. By fundamentally transforming the approach to quantum error handling, the Project will accelerate the transition from experimental quantum systems to reliable, scalable quantum computing platforms, aiming to ensure a leading edge for Canada in the global technological landscape

In quantum mechanical momentum theory, Clebsch-Gordan (CG) transforms coefficients can measure the degree of momentum entanglement in molecules. The complexity of the degree of entanglement generated by molecular orbital interaction needs to be elucidated in order to develop efficient quantum algorithms based on quantum mechanics, particularly under Coulomb and external fields. The potential of solid harmonic Gaussian orbitals (SHGOs), which are eigenfunctions of the angular momentum operator, has been largely underestimated for this purpose. Using SHGOs, Concordia University (CU) aims to develop an atom-centred angular momentum basis, a projection operator of c fermions acting on spherical harmonics. Compared with the existing tensor hypercontraction (THC) method, this new approach could overcome the need for computationally intensive density adjustment. In the orthogonal, unitary angular momentum basis, we can diagonalize the Coulomb operator. CU can derive a highly efficient quantum algorithm for simulating the electronic Hamiltonian using spherical harmonics as the projection function. The total number of atom-centred angular momentum basis functions is smaller than that of the atomic basis of an original molecular Hamiltonian. This new angular momentum algorithm can achieve O(N) scaling and reduce T complexity by several orders of magnitude compared with state-of-the-art THC methods. Integrating quantum simulation and machine learning into the project enables the team to use available NISQ devices and more mature classical computing techniques using GPUs and CPUs. The strategy involves increasing the complexity of the molecular systems generated as larger quantum computing systems are targeted.

Electronic circuits specially adapted to the control of “intelligent” multi-terabit optical transceivers will be developed for intra-satellite switching systems for the deployment of future space satellite networks in low earth orbit (LEO). The expected deployment of several thousand LEO satellites for large-scale global space connectivity by OneWeb, SpaceX and others requires reliable and incredibly fast connections within the satellite itself. These types of satellites are actually huge data routers—similar to those in terrestrial networks—needed to switch several terabits/s of data over free-space optical laser links from other satellites and the ground. However, they will also have to do this while travelling at speeds in excess of 18,000 km/h and being bombarded by high-energy particles. This project will require robust designs of advanced integrated circuits for fibre-optic transceivers with a high number of high-speed channels for relaying data between computer chips inside a satellite. In order to achieve characteristics and performance suitable for intra-satellite applications, the planned transceivers will be based on ring resonator technology coupled with the NRC’s quantum box comb lasers (QBCs). Electronic circuits will have to be specially designed to match these optical technologies.

Environmental Barrier Coatings (EBCs) are a key technology for the protection of ceramic matrix composites (CMCs) from the corrosion effects of water vapour at high temperatures. The use of ceramic-based turbine materials provides higher fuel efficiency, cost and fuel savings, and lower environmental impact. This Project seeks to decrease the porosity of thermal sprayed EBCs by application of a sealant material, so that the coatings become impermeable to water vapour. Sealant powders will be prepared and applied to the coating using slurry methods or by applying the chemical precursors directly on the plasma sprayed coating. The latter approach constitutes a novel way to a prepare a coating that will potentially display self-healing properties. The potential outcomes of this research Project include a complementary method for the application of dense EBCs on CMCs, and a more fundamental understanding of the behaviour of these materials at high temperature in the presence of water vapour. The results of this Project will benefit Canadian companies from powder manufacturers, equipment providers, coating applicators, essentially over the entire value chain.

This project will develop a physics informed machine learning approach to predicting battery durability to be implemented into a self-driving laboratory being developed by the NRC. It leverages University of Toronto AI-assisted tools for fitting electrochemical impedance spectroscopy (EIS), a central tool in measuring electrochemical systems, and generating statistically significant and unbiased models of physical processes. The Recipient will refine and apply this tool to support the NRC’s development of novel battery cathodes by developing (1) an automated sensitivity analysis and out of distribution detection algorithm to enable model updating during active learning studies, (2) a robust modeling framework for generating physical insights into battery performance and degradation, which will permit scientifically informed adjustments to battery formulations, and (3) an active learning tool that combines these tools to predict battery longevity without long term cycling studies. All data and code generated will be released publicly to benefit all Canadians.