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DTSTART;TZID=America/Chicago:20251121T080000
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UID:submissions.supercomputing.org_SC25_sess620_post260@linklings.com
SUMMARY:Bridging the Quantum Coding Gap: Instruction-Tuned LLMs for Qiskit
DESCRIPTION:Sixu Chen, Yuqi Zhang, and Qiang Guan (Kent State University)\
 n\nLarge language models (LLMs) have advanced code generation ability acro
 ss many domains, but often struggle with quantum code due to limited domai
 n-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 impo
 rtantly, we construct instruction-style prompt/completion pairs based on r
 eal-world Qiskit code to improve alignment during fine-tuning. Experiments
  show that our fine-tuned models significantly improve quantum code genera
 tion ability, validating the effectiveness of our approach.\n\nTag: Resear
 ch & ACM SRC Posters\n\nRegistration Category: Technical Program Reg Pass\
 n\nSession Chairs: Kento Sato (RIKEN Center for Computational Science (R-C
 CS)); Anja Gerbes (Georg-August-Universität Göttingen); and Chris Schlipal
 ius (Pawsey Supercomputing Research Centre; Commonwealth Scientific and In
 dustrial Research Organisation (CSIRO), Australia)\n\n
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