Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations
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.
| Authors | Hongquan Wang, Hanshu Chen, Ilia Marchevsky et al. |
|---|---|
| Primary category | quant-ph |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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