This program is tentative and subject to change.

Wed 17 Jul 2024 16:36 - 16:54 at Baobá 6 - AI4SE 2

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)-based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated QLEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-ofthe-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.

This program is tentative and subject to change.

Wed 17 Jul

Displayed time zone: Brasilia, Distrito Federal, Brazil change

16:00 - 18:00
16:00
18m
Talk
MonitorAssistant: Simplifying Cloud Service Monitoring via Large Language Models
Industry Papers
Zhaoyang Yu Tsinghua University, Minghua Ma Microsoft Research, Chaoyun Zhang Microsoft, Si Qin Microsoft Research, Yu Kang Microsoft Research, Chetan Bansal Microsoft Research, Saravan Rajmohan Microsoft, Yingnong Dang Microsoft Azure, Changhua Pei Computer Network Information Center at Chinese Academy of Sciences, Dan Pei Tsinghua University, Qingwei Lin Microsoft, Dongmei Zhang Microsoft Research
16:18
18m
Talk
Code-Aware Prompting: A study of Coverage guided Test Generation in Regression Setting using LLM
Research Papers
Gabriel Ryan Columbia University, Siddhartha Jain AWS AI Labs, Mingyue Shang AWS AI Labs, Shiqi Wang AWS AI Labs, Xiaofei Ma AWS AI Labs, Murali Krishna Ramanathan AWS AI Labs, Baishakhi Ray Columbia University, New York; AWS AI Lab
16:36
18m
Talk
A Machine Learning-Based Error Mitigation Approach for Reliable Software Development on IBM’s Quantum Computers
Industry Papers
Asmar Muqeet Simula Research Laboratory and University of Oslo, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University, Tao Yue Beihang University, Paolo Arcaini National Institute of Informatics
16:54
18m
Talk
Multi-line AI-assisted Code Authoring
Industry Papers
Omer Dunay Meta Platforms, Inc., Daniel Cheng Meta Platforms Inc., Adam Tait Meta Platforms, Inc., Parth Thakkar Meta Platforms, Inc., Peter C Rigby Meta / Concordia University, Andy Chiu Meta Platforms, Inc., Imad Ahmad Meta Platforms, Inc., Arun Ganesan Meta Platforms, Inc., Chandra Sekhar Maddila Meta Platforms, Inc., Vijayaraghavan Murali Meta Platforms Inc., Ali Tayyebi Meta Platforms Inc., Nachiappan Nagappan Meta Platforms, Inc.
17:12
18m
Talk
Combating Missed Recalls in E-commerce Search: a CoT-prompting Testing Approach
Industry Papers
Shengnan Wu School of Computer Science, Fudan University, Yongxiang Hu Fudan University, Yingchuan Wang School of Computer Science, Fudan University, Jiazhen Gu The Chinese University of Hong Kong, Jin Meng Meituan Inc., Liujie Fan Meituan Inc., Zhongshi Luan Meituan Inc., Xin Wang Fudan University, Yangfan Zhou Fudan University
17:30
18m
Talk
An Empirically Grounded Path Forward for Scenario-based Testing of Autonomous Driving Systems
Industry Papers
Qunying Song Lund University, Emelie Engstrom Lund University, Per Runeson Lund University
17:48
9m
Talk
ConDefects: A Complementary Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair
Demonstrations
Yonghao Wu Beijing University of Chemical Technology, Zheng Li Beijing University of Chemical Technology, Jie M. Zhang King's College London, Yong Liu Beijing University of Chemical Technology