6 papers
Compiling Code LLMs into Lightweight Executables
Jieke Shi, Junda He, Zhou Yang +6
The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have prod…
QUT: A Unit Testing Framework for Quantum Subroutines
Mykhailo V. Klymenko, Thong Hoang, Hoa Nguyen +6
We present the architectural design and prototype implementation of QUT (Quantum Unit Testing), a framework for unit testing of quantum subroutines. The framework is developed with…
ESG Reporting Lifecycle Management with Large Language Models and AI Agents
Thong Hoang, Mykhailo Klymenko, Xiwei Xu +6
Environmental, Social, and Governance (ESG) standards have been increasingly adopted by organizations to demonstrate accountability towards ethical, social, and sustainability goal…
EulerESG: Automating ESG Disclosure Analysis with LLMs
Yi Ding, Xushuo Tang, Zhengyi Yang +12
Environmental, Social, and Governance (ESG) reports have become central to how companies communicate climate risk, social impact, and governance practices, yet they are still publi…
Context-Aware Unit Testing for Quantum Subroutines
Mykhailo Klymenko, Thong Hoang, Samuel A. Wilkinson +6
Software testing is a critical component of the classical software development lifecycle, and this principle is expected to hold true for quantum software as it evolves toward larg…
Architectural Patterns for Designing Quantum Artificial Intelligence Systems
Mykhailo Klymenko, Thong Hoang, Xiwei Xu +4
Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversa…