Reference number : 172-2023-2024-Q4-1016014, amendment number : , agreement type : G, recipient legal name : The Governors of the University of Calgary, recipient country : CA, recipient province : AB, recipient city : Calgary|Calgary, recipient postal code : T2N 1N4, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Hybrid quantum-classical computing for quantum chemistry and machine learning, agreement title french : Méthode hybride de calcul quantique et de calcul classique pour la chimie quantique et l’apprentissage automatique, agreement number : 1016014, agreement value : 323400.0, agreement start date : 2024-03-20, agreement end date : 2026-03-30, description english : Current efforts to design materials computationally face two bottlenecks: accuracy and speed. The workhorse density-functional theory (DFT) computational methods are time-consuming and of limited accuracy for strongly correlated systems like the metal-oxides commonly used for catalysis. (Classical) Machine learning (ML) is showing great promise in reducing the number of expensive DFT calculations in the design process. Quantum computing (focusing on hybrid approaches using Noisy Intermediate Scale Quantum (NISQ) computers for parts of the problem) is showing great promise to contribute to opening both bottlenecks by facilitating the use of more accurate quantum chemical methods and accelerating machine-learning approaches beyond the possibilities of classical ML. The proposed research will be directed along two complementary streams: i) quantum computing for quantum chemistry and ii) quantum machine learning., description french : Méthode hybride de calcul quantique et de calcul classique pour la chimie quantique et l’apprentissage automatique, expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada