Building a fault-tolerant quantum computer is one of the hardest engineering challenges of our time — spanning chip design and fabrication, system engineering and control, error correction, quantum architecture, quantum software, and quantum algorithms. At Xanadu, we found that the bottleneck isn’t just physics; it’s the sheer cognitive overhead of orchestrating research across dozens of interdependent teams, simulators, codebases, and datasets.

AI, with its recent advancements, has become a natural part of how we tackle this complexity — not as a side project, but as core infrastructure. Agentic systems now operate in the background across our R&D pipeline, autonomously navigating codebases, running analyses, and connecting insights across teams. Combined with ML-driven tools built in-house for tasks like photonic chip characterization and design optimization, these systems are compressing feedback loops that once took days into minutes.

This talk explores how the convergence of agentic AI and domain-specific machine learning is quietly reshaping the pace of quantum computing R&D. We share lessons learned from deploying these tools in a real hardware program, the cultural shift required to trust AI as a research partner, and why we believe the road to fault tolerance runs through AI as much as it runs through physics.