⚛️ Physics

🤖 AAAI2026 · 2 paper notes

Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness

This paper proposes QuFid, a framework that models quantum circuits as directed acyclic graphs (DAGs), characterizes noise propagation via control-flow-aware random walks, quantifies circuit complexity through spectral features of the propagation operator, and achieves adaptive measurement budget allocation — significantly reducing the number of measurement shots while maintaining fidelity accuracy.

Data Verification is the Future of Quantum Computing Copilots

This position paper argues that data verification must be elevated from a post-hoc filtering step to a foundational architectural principle in quantum computing AI copilots. Three positions are advanced: (1) verified data is a minimum requirement; (2) prior constraints outperform posterior filtering; (3) scientific domains governed by physical laws require verification-aware architectures. Experiments demonstrate that LLMs trained without verified data achieve at most 79% accuracy on circuit optimization tasks.