The role of machine learning in quantum software and compilation

Olivia Di Matteo introduces quantum computing and compilation, examining how the translation from high-level quantum programs to efficient hardware instructions becomes a bottleneck as applications scale. The seminar identifies parts of the quantum software stack where machine learning and artificial intelligence can improve this process. It presents the group’s work using graph-based reinforcement learning to optimize quantum circuits, then considers outstanding challenges and research questions for scalable quantum software.