CX-271058: Lawrence Livermore National Laboratory - Machine Learning Assisted Generation of Next-Generation Electronic and Thermodynamic Innovations through Quantum Computing (MAGNETIQC)

Funding will support the project team's small-scale research and development of quantum and machine learning-accelerated software tools and apply them to discovering ultra-strong, lightweight magnets for electric motors and generators and for future high-performance information technology. Specifically, the project team will (1) develop, test, and validate a quantum hybrid algorithm, (2) optimize subroutines and algorithms to fit hardware, (3) scale algorithm, test and validate on hardware platform to meet performance metrics, (4) develop magnetic materials database and incorporate into quantum algorithm, (5) identify magnetic materials with highest values using quantum algorithm to meet application targets, (6) test and validate to confirm magnetic materials values, and (7) conduct a techno-economic analysis and life cycle assessment for next phase of development. If successful, this project will result in a proven quantum algorithm to identify lower cost, effective magnetic materials to advance energy applications, ultimately reducing dependence on foreign energy and minerals, and thereby increasing U.S. energy security.