The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Sixu Chen (Kent State University), Yuqi Zhang (Kent State University), Qiang Guan (Kent State University)
Supervisor:
Abstract: Large language models (LLMs) have advanced code generation ability across many domains, but often struggle with quantum code due to limited domain-specific data and inherent domain complexity. To address this issue, we focus on the Qiskit framework and fine-tune pretrained LLMs using quantum code from GitHub and datasets including OASST1 and COMMITPACKFT. More importantly, we construct instruction-style prompt/completion pairs based on real-world Qiskit code to improve alignment during fine-tuning. Experiments show that our fine-tuned models significantly improve quantum code generation ability, validating the effectiveness of our approach.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF