DeepONet enables retraining-free inference across varying initial conditions or source terms at the cost of high computational requirements. This paper proposes a hybrid quantum operator network (Quantum AS-DeepOnet) suitable for solving 2D evolution equations. By combining Parameterized Quantum Circuits and cross-subnet attention methods, we can solve 2D evolution equations using only 60% of the trainable parameters while maintaining accuracy and convergence comparable to the classical DeepONet method.
Cite
@article{arxiv.2603.02261,
title = {Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations},
author = {Hongquan Wang and Hanshu Chen and Ilia Marchevsky and Zhuojia Fu},
journal= {arXiv preprint arXiv:2603.02261},
year = {2026}
}