Quantum DeepONet

In this talk, I introduced quantum DeepONet, a model that integrates quantum computing with operator learning for solving PDEs. I explained how quantum circuits accelerate matrix multiplication in neural networks, compared its complexity with classical methods, and discussed quantum noise challenges along with error mitigation techniques. Finally, I presented benchmark results on various PDEs, demonstrating the potential of quantum DeepONet in practical applications.

Feb 28, 2025