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arXiv Machine Learning By Purav Matlia, Christian Moya, Guang Lin

Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty

Read the original on arXiv Machine Learning →

arXiv:2605. 00330v2 Announce Type: replace Abstract: Operator learning enables fast surrogate modeling of high-dimensional dynamical systems, but existing approaches face two fundamental limitations: quadratic inference complexity and unreliable uncertainty quantification in safety-critical settings.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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