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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.02261 |
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| _version_ | 1866910038807805952 |
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| author | Wang, Hongquan Chen, Hanshu Marchevsky, Ilia Fu, Zhuojia |
| author_facet | Wang, Hongquan Chen, Hanshu Marchevsky, Ilia Fu, Zhuojia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02261 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations Wang, Hongquan Chen, Hanshu Marchevsky, Ilia Fu, Zhuojia Quantum Physics Machine Learning 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. |
| title | Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2603.02261 |