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

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Hongquan Wang, Hanshu Chen, Ilia Konstantinovich Marchevsky, Zhuojia Fu
DOI: 10.48550/arxiv.2603.02261
Cited by: 0

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.

Fields of Study & Subjects

Model Reduction and Neural Networks Quantum Computing Algorithms and Architecture Neural Networks and Applications

Publication details

Journal / source
arXiv (Cornell University)
Publication date
28 February 2026
Language
EN
Document type
Preprint