Quantum DeepONet: Neural operators accelerated by quantum computing

The data and code for the paper P. Xiao, M. Zheng, A. Jiao, X. Yang, & L. Lu. Quantum DeepONet: Neural operators accelerated by quantum computing. Quantum, 9, 1761, 2025.

Datasets

Data generation scripts are available in the data folder:

• Antiderivative
• Poisson's equation
• Advection equation
• Burgers equation

Each script generates training and testing data for the respective problem.

Code

All code is in the folder src. The code depends on the deep learning package DeepXDE v1.10.1.

To install dependencies:

pip install -r requirements.txt

To train a model for a specific task, navigate to the corresponding example directory and run:

python training.py

After training, simulate the trained quantum DeepONet using Qiskit. The simulation scripts are located in the same folder as the training code. To run the simulation, use:

python simulation.py

Note: Some tasks may use different script names for simulation; please check the example folder for details.

Data-driven

• Function 1
• Function 2
• Antiderivative
• Advection equation
• Burgers' equation

Physics-informed

• Antiderivative
• Poisson's equation

Cite this work

If you use this data or code for academic research, you are encouraged to cite the following paper:

@article{Xiao2025quantumdeeponet,
  author  = {Xiao, Pengpeng and Zheng, Muqing and Jiao, Anran and Yang, Xiu and Lu, Lu},
  title   = {Quantum {D}eep{ON}et: {N}eural operators accelerated by quantum computing}, 
  journal = {{Quantum}},
  volume  = {9},
  number  = {},
  pages   = {1761},
  year    = {2025},
  doi     = {https://doi.org/10.22331/q-2025-06-04-1761}
}

Question

To get help on how to use the data or code, simply open an issue in the GitHub "Issues" section.