Quantum Intermediate Representation (QIR) is an LLVM-based intermediate representation developed by Microsoft for quantum program compilers. QIR is designed to offer a universal solution for quantum program compilers, decoupled from both front-end languages and back-end hardware, thereby eliminating the need for redundant development of intermediate representations and compilers. However, the lack of a formal definition and reliance on natural language descriptions in the current state of QIR result in interpretational ambiguity and a dearth of rigor in implementing quantum functions. In this paper, we present formal definitions for QIR’s data types and instruction sets to establish correctness and safety assurances for operations and intermediate code conversions within QIR. To demonstrate the effectiveness of our approach, we provide examples of unsafe QIR codes where errors can be identified with our method.
| Enhancing Code Safety in Quantum Intermediate Representation (ase23-nier-25.pdf) | 374KiB |
Wed 13 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
15:30 - 17:00 | Code Generation 2Research Papers / NIER Track / Tool Demonstrations at Plenary Room 2 Chair(s): Marianne Huchard LIRMM | ||
15:30 12mTalk | COMEX: A Tool for Generating Customized Source Code Representations Tool Demonstrations Debeshee Das Indian Institute of Technology Tirupati, Noble Saji Mathews University of Waterloo, Canada, Alex Mathai , Srikanth Tamilselvam IBM Research, Kranthi Sedamaki Indian Institute of Technology Tirupati, Sridhar Chimalakonda IIT Tirupati, Atul Kumar IBM India Research Labs Pre-print Media Attached File Attached | ||
15:42 12mTalk | Fast and Reliable Program Synthesis via User Interaction Research Papers Yanju Chen University of California at Santa Barbara, Chenglong Wang Microsoft Research, Xinyu Wang University of Michigan, Osbert Bastani University of Pennsylvania, Yu Feng University of California at Santa Barbara File Attached | ||
15:55 12mTalk | From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining Research Papers Xiaoxue Ren Zhejiang University, Xinyuan Ye Australian National University, Dehai Zhao CSIRO's Data61, Zhenchang Xing , Xiaohu Yang Zhejiang University File Attached | ||
16:08 12mTalk | Generative Type Inference for Python Research Papers Yun Peng Chinese University of Hong Kong, Chaozheng Wang The Chinese University of Hong Kong, Wenxuan Wang Chinese University of Hong Kong, Cuiyun Gao Harbin Institute of Technology, Michael Lyu The Chinese University of Hong Kong Pre-print File Attached | ||
16:21 12mTalk | Compiler Auto-tuning via Critical Flag Selection Research Papers | ||
16:34 12mTalk | Enhancing Code Safety in Quantum Intermediate Representation NIER Track File Attached | ||
16:47 12mTalk | CAT-LM: Training Language Models on Aligned Code And Tests Research Papers Nikitha Rao Carnegie Mellon University, Kush Jain Carnegie Mellon University, Uri Alon Carnegie Mellon University, Claire Le Goues Carnegie Mellon University, Vincent J. Hellendoorn Carnegie Mellon University Media Attached File Attached | ||