用量子注意力网络减少参数量，高效求解二维演化方程
Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations
- 结合量子线路与跨子网注意力机制，构建混合量子算子网络
- 仅需60%参数量，精度和收敛性媲美经典DeepONet
- 适合需要低参数高效率的科学计算场景
DeepONet 能在不重新训练的情况下处理变化的初值或源项，但计算开销大。本文提出一种适用于求解二维演化方程的混合量子算子网络（Quantum AS-DeepOnet），通过将参数化量子电路与跨子网注意力方法结合，在保持与经典DeepONet相当的精度和收敛性的同时，将可训练参数量减少至60%。
原文摘要 · Abstract (English)
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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