Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
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. We propose Conformalized Quantum DeepONet Ensembles, a framework that addresses both challenges simultaneously. By leveraging Quantum Orthogonal Neural Networks (QOrthoNNs), we reduce operator inference complexity from O(n^2) to O(n), enabling scalable evaluation over fine discretizations. To provi
The continuous push for more efficient and robust AI models, especially in complex system simulations, drives innovation in quantum-enhanced AI at this moment.
This development addresses critical limitations in existing operator learning models—computational scalability and reliable uncertainty quantification—which are crucial for deploying AI in safety-critical applications.
Operator learning could become significantly more scalable and trustworthy, enabling faster, more reliable surrogate modeling for high-dimensional dynamical systems in fields like scientific computing and engineering.
- · Quantum computing researchers
- · AI/ML developers
- · Engineering and scientific simulation platforms
- · High-performance computing sector
- · Traditional quadratic complexity deep learning models
- · Systems reliant on computationally intensive simulations
More accurate and faster simulations of complex physical systems become possible due to reduced inference complexity and improved uncertainty quantification.
This could accelerate R&D cycles in areas like drug discovery, materials science, and climate modeling by enabling rapid, reliable experimentation with virtual models.
The integration of quantum methods into classical AI workflows may become a standard practice, reshaping how industries approach design, optimization, and risk assessment.
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Read at arXiv cs.LG