This research demonstrates how Large Language Models can be fine-tuned to automatically generate efficient quantum circuits at scale, addressing a significant gap in quantum computing.

• Successfully adapts LLM capabilities to the specialized domain of quantum computing
• Injects domain-specific quantum knowledge into foundation models
• Enables automated generation of quantum circuits for optimization problems
• Creates a scalable approach for quantum circuit design

This advancement is crucial for engineering as it bridges AI and quantum computing, potentially accelerating quantum algorithm development and making quantum computing more accessible to engineers without specialized quantum expertise.

Fine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation