- By:
- Afrose, Sharmin ; Leyton Ortega, Vicente A; Humble, Travis S
- Page Number:
- 576-577
- Book Title:
- IEEE International Conference on Quantum Computing and Engineering (QCE)
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- IEEE International Conference on Quantum Computing and Engineering (QCE)
- Conference Location:
- Albuquerque, New Mexico, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/QCE65121.2025.10453
Abstract
We propose a learning-based approach to quantum compilation by translating OpenQASM to Quantum Intermediate Representation (QIR) using a fine-tuned CodeBERT model. Trained on 10,000 synthetic QASM-QIR pairs, the model captures code semantics while addressing QIR verbosity and the 512-token limit via a custom token compression scheme. Finetuning was performed on the Frontier supercomputer, with results showing syntactic correctness and stable validation loss reduction. Our method moves toward enabling flexible, language-modeldriven quantum software tools. It also introduces syntax error handling and the possibility of incorporating classical control constructs, addressing limitations in existing rule-based compilers like qBraid-QIR. While the current model has been validated on quantum-only circuits, we propose future evaluations on hybrid quantum-classical examples. This poster will provide architecture insights, compression examples, training loss plots, and QIR outputs. Our work highlights the potential for scalable, adaptable compilation in future quantum toolchains.
