Grants and Contributions:

Title:

AI-Enhanced Quantum Computing Algorithms and Simulations Based on Entanglement for Advanced Quantum Defense Science and Technology Applications

Agreement Number:

1016614

Agreement Value:

$264,000.00

Agreement Date:

Mar 26, 2024 - Mar 1, 2026

Description:

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.

Organization:

National Research Council Canada

Expected Results:

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.

Location:

Montreal, Quebec, CA H3G 1M8

Reference Number:

172-2023-2024-Q4-1016614

Agreement Type:

Grant

Report Type:

Grants and Contributions

Recipient Business Number:

106966591

Recipient Type:

Academia

Recipient's Legal Name:

Concordia University

Federal Riding Name:

Ville-Marie–Le Sud-Ouest–Île-des-Soeurs

Federal Riding Number:

24077

Program:

Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives

Program Purpose:

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.

NAICS Code:

541710 - R&D in the physical, engineering and life sciences