Conformalized Quantum DeepONet Ensembles: Towards Scalable Operator Learning with Distribution-Free Guarantees
Read the original on arXiv Machine Learning →The paper introduces Conformalized Quantum DeepONet Ensembles, a framework that combines Quantum Orthogonal Neural Networks (QOrthoNNs) with split conformal calibration to address the quadratic cost of dense neural layers and unreliable uncertainty quantification in operator learning. It demonstrates that QOrthoNNs achieve ×O(n) hidden‑layer running‑time scaling, improving on the ×O(n^2) cost of classical dense layers, while the ensemble approach provides a finite‑sample, distribution‑free lower bound on coverage for new input‑output function pairs. Experiments on synthetic benchmarks and real‑world power‑system dynamics confirm accurate predictions and empirical coverage close to the target under both ideal and noisy quantum simulations.
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