Presentation
Quantum and AI Convergence
DescriptionQuantum and AI are converging from chips to systems—a cross-layer opportunity. I'll position quantum-AI as design automation: noise-aware synthesis/compilation and verification mapped to real devices; interpretable QNNs (Quantum Grad-CAM) for debug and coverage; privacy-preserving and federated QML for secure, distributed training; "learning to measure" that treats observables as tunable design knobs; and multi-chip ensemble VQCs that improve trainability and robustness under realistic constraints. We'll show how these plug into EDA flows—accelerating design-space exploration, improving PPA/quality-of-results, and reducing calibration/iteration time—on workloads from HEP analytics to power-grid control. I'll close with an integration agenda for QAI toolchains, metrics, and benchmarks that hardware and CAD teams can use now while we scale to larger systems.
Event Type
Research Special Session
TimeWednesday, July 2912:00pm - 12:30pm PDT
LocationMtg Room 201A