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This research demonstrates how quantum-compressed machine learning (QCML) resolves the expressivity-explainability paradox in scientific modeling by drastically reducing trainable parameters while maintaining stability for complex fluid flows. By constraining latent spectra to physical constitutive relations, this approach offers a viable path toward practical quantum advantage where classical deep surrogates typically fail due to error accumulation over time.

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Quantum Circuit Compresses Flow Surrogates to Fewer Than 100 Parameters

Quantum Zeitgeist1 august 20265 min de cititArticolul și perspectiva sunt în engleză.

Explainable quantum-compressed machine learning for complex fluid flows AUTHORS AND AFFILIATIONS: Xiao Xue † Centre for Computational Science, University College London, London, UK Maida Wang † Centre for Computational Science, University College London, London, UK Mingyang Gao Centre for Computational Science, University College London, London, UK Minh Chung Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities Garching, Germany Peter V. Coveney Centre for Computational Science, University College London, London, UK Centre for Advanced Research Computing, University College London, London, UK Explainable quantum-compressed machine learning for complex fluid flows Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box.

Here’s the corrected article, with only the specified numbers changed to match the source: University College London and the Leibniz Supercomputing Centre have developed a quantum-compressed machine learning (QCML) method that reduces the trainable parameters of a flow surrogate to no more than. This parameter reduction positions the learned dynamics closer to the scale of a physical constitutive relation, rather than a complex, uninterpretable neural network. The work addresses a key challenge in scientific machine learning: creating explainable models capable of capturing complex,…

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