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.
A two-copy Bell-measurement readout of a trained quantum prior estimates arbitrary Pauli statistics of an invariant measure with copy count independent of qubit number, while single-copy readout needs exponentially many copies; case studies show forecast and rollout gains.
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SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts
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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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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Practical Quantum Advantage before Fault Tolerance via Quantum-Informed Machine Learning
A two-copy Bell-measurement readout of a trained quantum prior estimates arbitrary Pauli statistics of an invariant measure with copy count independent of qubit number, while single-copy readout needs exponentially many copies; case studies show forecast and rollout gains.