Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations
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Abstract (arXiv)
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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References · 7
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
- 2018Supervised learning with quantum enhanced feature spacescited by 665
- 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 575
- 2019Parameterized quantum circuits as machine learning modelscited by 332
- 2020nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applicationscited by 36
- 2021Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementationcited by 23
- 2025QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEscited by 9
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