Funder: Institute for Pure and Applied Mathematics
Due Dates: November 30, 2026 (Applications for fullest consideration) | January 20–22, 2027 (Winter school)
Funding Amounts: Attendance support available, especially for graduate students, recent PhDs, and early-career researchers
Summary: Training and attendance support for researchers exploring large language models and cooperating AI agents in quantum research.
Key Information: All participants, including UCLA participants and those not requesting funding, must apply and receive an invitation.
Description
The Institute for Pure and Applied Mathematics (IPAM), the Challenge Institute for Quantum Computation, and the Quantum Computing Science Association present a winter school on LLM-based agentic networks in quantum information science. Intended for graduate students and early-career researchers in quantum information science, physics, computer science, and applied mathematics, the school explores AI support for theoretical, computational, and experimental research.
Tutorials and hands-on sessions introduce planning, tool use, and multi-agent coordination. Applications include complexity theory, quantum circuit discovery and optimization, quantum simulation, and experimental automation. The program emphasizes verifying AI-generated results, establishing meaningful benchmarks, and understanding human judgment. By bringing experimentalists, theorists, and AI researchers together, it aims to develop reliable AI-assisted research methods and new collaborations.
