6 papers
Explainable quantum-compressed machine learning for complex fluid flows
Xiao Xue, Maida Wang, Mingyang Gao +2
Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep…
Practical Quantum Advantage before Fault Tolerance via Quantum-Informed Machine Learning
Maida Wang, Xiao Xue, Minh Chung +1
Early quantum devices can deliver a practical advantage before fault tolerance. The role we identify is a statistical module within a classical scientific workflow: a compressed me…
Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
Maida Wang, Xiao Xue, Mingyang Gao +1
We introduce a quantum-informed machine learning (QIML) framework for modelling the long-term behaviour of high-dimensional chaotic systems. QIML combines a one-time, offline-train…
On the Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction
Shunzhou Wan, Xibei Zhang, Xiao Xue +1
Despite continuing hype about the role of AI in drug discovery, no "AI-discovered drugs" have so far received regulatory approval. Here we assess one of the latest AI based tools i…
An Uncertainty Visualization Framework for Large-Scale Cardiovascular Flow Simulations: A Case Study on Aortic Stenosis
Xiao Xue, Tushar M. Athawale, Jon W. S. McCullough +5
We present a generalizable uncertainty quantification (UQ) and visualization framework for lattice Boltzmann method simulations of high Reynolds number vascular flows, demonstrated…
A Multi-Component, Multi-Physics Computational Model for Solving Coupled Cardiac Electromechanics and Vascular Haemodynamics
Sharp C. Y. Lo, Alberto Zingaro, Jon W. S. McCullough +5
The circulatory system, comprising the heart and blood vessels, is vital for nutrient transport, waste removal, and homeostasis. Traditional computational models often treat cardia…