[2603.02261] Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations

Quantum Physics arXiv:2603.02261 (quant-ph) [Submitted on 28 Feb 2026] Title:Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations Authors:Hongquan Wang, Hanshu Chen, Ilia Marchevsky, Zhuojia Fu View a PDF of the paper titled Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations, by Hongquan Wang and 3 other authors View PDF HTML (experimental) 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. Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2603.02261 [quant-ph]   (or arXiv:2603.02261v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.02261 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhuojia Fu Prof. [view email] [v1] Sat, 28 Feb 2026 07:07:17 UTC (1,324 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations, by Hongquan Wang and 3 other aut...