CAREER: Efficient and Scalable Deployment Automation for Quantum-Centric Computing

As computing infrastructure gradually transitions from classical high-performance computing to quantum-centric computing cyberinfrastructure (QuCI) with quantum computers, classical computers, and AI accelerators, QuCI is expected to revolutionize domain applications such as computational chemistry, material science, and combinatorial optimizations, outperforming state-of-the-art classical computing significantly regarding speed, accuracy, or the ability to handle larger problem sizes. This project proposes an automated QuCI deployment framework (AutoQC) to perform quantum application deployment automatically. Beyond the new technology, the project also supports a cohort of education and outreach activities, including hosting workshop series at regional minority-serving institutions, organizing events at interdisciplinary communities (e.g., workshops, tutorials, competitions), initializing K-12 programs, and developing and updating curriculum. One key component in these activities is developing a novel visualization tool that makes the decision-making process visible. It also serves as an educational tool for curriculum and outreach activities to lower the learning bar for beginners. The project designs automated, efficient, scalable AutoQC algorithms across full-stack deployment layers, including resource allocation, circuit compilation, and functional circuit design layers. Specifically, this project includes (1) designing a high-quality AI-powered quantum performance predictor, yielding an innovative fidelity-aware QuCI resource allocation tool; (2) developing an application-specific pulse-level compilation algorithm to improve system stability at run-time on noisy quantum devices; and (3) building an automated quantum circuit search technique to construct logical quantum gates for a specific quantum error correction code. This research agenda enables the automated deployment of a given quantum circuit to physical quantum bits with improved usability, efficiency, scalability, and reliability on QuCIs at both near-term noisy intermediate-scale quantum computing and long-term fault-tolerant quantum computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.