Maida Wang

Xiao Xue, Tianyue Yang, Mingyang Gao et al.

Spatiotemporal flows govern diverse phenomena across physics, biology, and engineering, yet modelling their multiscale dynamics remains a central challenge. Despite major advances in physics-informed machine learning, existing approaches struggle to simultaneously maintain long-term temporal evolution and resolve fine-scale structure across chaotic, turbulent, and physiological regimes. Here, we introduce Uni-Flow, a unified autoregressive-diffusion framework that explicitly separates temporal evolution from spatial refinement for modelling complex dynamical systems. The autoregressive component learns low-resolution latent dynamics that preserve large-scale structure and ensure stable long-horizon rollouts, while the diffusion component reconstructs high-resolution physical fields, recovering fine-scale features in a small number of denoising steps. We validate Uni-Flow across canonical benchmarks, including two-dimensional Kolmogorov flow, three-dimensional turbulent channel inflow generation with a quantum-informed autoregressive prior, and patient-specific simulations of aortic coarctation derived from high-fidelity lattice Boltzmann hemodynamic solvers. In the cardiovascular setting, Uni-Flow enables task-level faster than real-time inference of pulsatile hemodynamics, reconstructing high-resolution pressure fields over physiologically relevant time horizons in seconds rather than hours. By transforming high-fidelity hemodynamic simulation from an offline, HPC-bound process into a deployable surrogate, Uni-Flow establishes a pathway to faster-than-real-time modelling of complex multiscale flows, with broad implications for scientific machine learning in flow physics.

Maida Wang, Xiao Xue, Mingyang Gao et al.

We introduce a quantum-informed machine learning (QIML) framework for the long-term dynamical behavior of high-dimensional chaotic systems. The method combines a one-time, offline-trained quantum generative model with a classical autoregressive predictor for spatiotemporal field generation. The quantum model learns a quantum prior (Q-Prior) that guides the representation of small-scale interactions and improves the modeling of fine-scale dynamics. We evaluate QIML on three representative systems: the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and a cross-section of fully developed three-dimensional turbulent channel flow used as a realistic inflow condition. Compared to the classical baseline, QIML yields up to 17.25% improvement in predictive distribution accuracy and a 29.36% improvement in the fidelity of the predicted full energy spectrum. For turbulent channel inflow, the Q-Prior is essential: without it, the model fails to evolve in time, while QIML produces stable, physically consistent forecasts that surpass leading machine learning models for PDEs, including the Fourier Neural Operator and Markov Neural Operator, whose errors diverge. Beyond accuracy, QIML also achieves a memory advantage, compressing multi-megabyte datasets into a kilobyte-scale Q-Prior that captures only the invariant measure needed to guide the classical model, thus circumventing Holevo's bound by avoiding full data reconstruction. Our findings provide a practical and scalable pathway for integrating the advantages brought by quantum devices into large-scale scientific, engineering modeling and simulation.

Maida Wang, Jinyang Jiang, Peter V. Coveney

Quantum machine learning has gained attention for its potential to address computational challenges. However, whether those algorithms can effectively solve practical problems and outperform their classical counterparts, especially on current quantum hardware, remains a critical question. In this work, we propose a novel quantum machine learning method, called Parameter-Efficient Quantum Anomaly Detection (PEQAD), for practical image anomaly detection, which aims to achieve both parameter efficiency and superior accuracy compared to classical models. Emulation results indicate that PEQAD demonstrates favourable recognition capabilities compared to classical baselines, achieving an average accuracy of over 90% on benchmarks with significantly fewer trainable parameters. Theoretical analysis confirms that PEQAD has a comparable expressivity to classical counterparts while requiring only a fraction of the parameters. Furthermore, we demonstrate the first implementation of a quantum anomaly detection method for general image datasets on a superconducting quantum processor. Specifically, we achieve an accuracy of over 80% with only 16 parameters on the device, providing initial evidence of PEQAD's practical viability in the noisy intermediate-scale quantum era and highlighting its significant reduction in parameter requirements.