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arXiv · 2603.02261

Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations

Hongquan Wang·Hanshu Chen·Ilia Marchevsky·Zhuojia Fu
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Abstract

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.

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Hongquan Wang, Hanshu Chen, Ilia Marchevsky, Zhuojia Fu. 2026-02-28. Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations. https://arxiv.org/abs/2603.02261

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