I-Corps: Quantum and Artificial Intelligence (AI)-Powered Knowledge Management System for Nuclear Fusion

This I-Corps project is based on the development of a quantum- and Artificial Intelligence (AI)- enabled management and decision support software for nuclear fusion energy and other systems. Many organizations operating advanced scientific and engineering facilities face significant challenges in effectively utilizing large volumes of fragmented operational knowledge, including technical documentation, maintenance records, troubleshooting logs, and experimental observations. The loss of institutional knowledge and the time required to locate critical information can lead to costly delays, reduced productivity, and inefficient decision making. This technology provides a conversational, data-driven platform that integrates disparate information sources into a unified and accessible knowledge center. It enables users to rapidly retrieve relevant information, identify patterns, and support operational decisions. This technology may be used in industry areas including fusion energy, advanced manufacturing, aerospace, semiconductor, and other knowledge-intensive industries where efficient management of technical expertise and operational data is essential for competitiveness and growth. This has the potential to reduce costs, improve workforce efficiency, accelerate innovation, and lower barriers to the adoption of advanced technologies. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a quantum- and artificial intelligence (AI)-enabled knowledge management platform that integrates large language models, retrieval-augmented generation, and quantum optimization methods for complex engineering systems. The technology combines multimodal retrieval-augmented generation techniques with domain-specific knowledge graphs and external document repositories to provide context-aware, source-grounded responses from operational and technical data. In addition, quantum-inspired optimization approaches, including annealing-based methods and Ising-model formulations, are employed to navigate high-dimensional parameter spaces and identify candidate operational strategies more efficiently than current approaches. A computational prototype has demonstrated the ability to organize fragmented knowledge, improve information retrieval, and support optimization-driven decision making in complex technical workflows. This technology may advance the state of quantum AI-assisted engineering operations while providing a scalable framework for knowledge management, decision support, and optimization in emerging nuclear fusion energy and other industrial applications. 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.