SGNO achieves stable long-horizon PDE rollouts by organizing autoregressive steps as spectral evolution updates with a constrained diagonal generator and learned correction, delivering a median 74.8% reduction in GMean100 error across ten APEBench tasks.
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QIML uses a quantum-trained Q-Prior to enhance classical autoregressive predictions of spatiotemporal chaos, improving accuracy by up to 17.25% and full-spectrum fidelity by up to 29.36% while enabling stable forecasts for 3D turbulent channel flow.
Develops k-indexed Q-Priors for quantum-informed ML on chaotic systems, proving a two-stage quantum-classical separation in measurement complexity and demonstrating it in turbulent flow and weather forecasting workflows.
citing papers explorer
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Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
QIML uses a quantum-trained Q-Prior to enhance classical autoregressive predictions of spatiotemporal chaos, improving accuracy by up to 17.25% and full-spectrum fidelity by up to 29.36% while enabling stable forecasts for 3D turbulent channel flow.
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Foundations of Practical Quantum Advantage in Quantum-Informed Machine Learning for Predicting Chaos
Develops k-indexed Q-Priors for quantum-informed ML on chaotic systems, proving a two-stage quantum-classical separation in measurement complexity and demonstrating it in turbulent flow and weather forecasting workflows.