5 papers
Can LLMs Solve Science or Just Write Code? Evaluating Quantum Solver Generation
Luciano Baresi, Domenico Bianculli, Maryse Ernzer +3
Large Language Models (LLMs) show strong capabilities in code generation, motivating their use in automated quantum solver development. However, in quantum computing, successful ex…
Randomized and Diverse Input State Generation for Quantum Program Testing
Maryse Ernzer, Seung Yeob Shin, Fabrizio Pastore +1
With the accelerating development of quantum technologies and their growing computational potential, quantum systems are being adapted for simulations and other critical tasks acro…
Beyond Rules: LLM-Powered Linting for Quantum Programs
Pietro Cassieri, Giuseppe Scanniello, Seung Yeob Shin +2
As quantum computing transitions from theoretical experimentation to its practical application, the reliability of quantum software has become a critical bottleneck. Traditional st…
Quantum Program Linting with LLMs: Emerging Results from a Comparative Study
Seung Yeob Shin, Fabrizio Pastore, Domenico Bianculli
Ensuring the quality of quantum programs is increasingly important; however, traditional static analysis techniques are insufficient due to the unique characteristics of quantum co…
Towards Generating Executable Metamorphic Relations Using Large Language Models
Seung Yeob Shin, Fabrizio Pastore, Domenico Bianculli +1
Metamorphic testing (MT) has proven to be a successful solution to automating testing and addressing the oracle problem. However, it entails manually deriving metamorphic relations…