Registered user since Mon 22 Jun 2015
Contributions
NIER Track
Wed 13 Sep 2023 16:34 - 16:47 at Plenary Room 2 - Code Generation 2 Chair(s): Marianne HuchardQuantum 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.
File AttachedResearch Papers
Thu 14 Sep 2023 11:18 - 11:30 at Room C - Software Testing for Specialized Systems 2 Chair(s): Zishuo Dingno description available
File AttachedAbstract: Quantum computing has emerged as a disruptive technology with the potential to revolutionize various industries. However, because quantum software is different in nature from classical software, developing software for quantum computers requires a paradigm shift in traditional software engineering approaches. This tutorial addresses the challenges and opportunities in quantum software engineering and explores state-of-the- art methods and techniques. Participants will gain insights into developing reliable and robust quantum software systems, taking into account the unique characteristics of quantum systems. The tutorial fosters collaboration between researchers and practitioners, providing a platform to exchange ideas and experiences in quantum software engineering.
Research Papers
Tue 12 Sep 2023 14:06 - 14:18 at Room C - Testing AI Systems 2 Chair(s): Lwin Khin SharThe reliability of decision-making policies is urgently important today as they have established the fundamentals of many critical applications, such as autonomous driving and robotics. To ensure reliability, there have been a number of research efforts on testing decision-making policies that solve Markov decision processes (MDPs). However, due to the deep neural network (DNN)-based inherit and infinite state space, developing scalable and effective testing frameworks for decision-making policies still remains open and challenging.
In this paper, we present an effective testing framework for decision-making policies. The framework adopts a generative diffusion model-based test case generator that can easily adapt to different search spaces, ensuring the practicality and validity of test cases. Then, we propose a termination state novelty-based guidance to diversify agent behaviors and improve the test effectiveness. Finally, we evaluate the framework on five widely used benchmarks, including autonomous driving, aircraft collision avoidance, and gaming scenarios. The results demonstrate that our approach identifies more diverse and influential failure-triggering test cases compared to current state-of-the-art techniques. Moreover, we employ the detected failure cases to repair the evaluated models, achieving better robustness enhancement compared to the baseline method.
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