Data Science, Machine Learning, and Optimization to Advance Quantum Computing

The SIAM Quantum Intersections Convening, held in October 2024, produced a report that stands as a clear call to action for mathematicians. This comes at a moment when funding opportunities in quantum computing research remain strong. At the same time, quantum science and engineering offer several research opportunities where established methods from data science, statistics, linear algebra, and optimization play a central role. In this talk, drawing on the guidance of the SIAM report, I will highlight areas where these mathematical tools can make an impact, suggest directions that may be of particular interest to applied mathematicians, and share my own experience of contributing to quantum science as someone who is not a quantum specialist.