1887

Abstract

Summary

Accurate solutions to the wave equation are crucial in geophysics, supporting applications such as seismic imaging and inversion by enabling detailed modeling of wave propagation through complex geological formations. Physics-Informed Neural Networks (PINNs) offer a promising approach by incorporating physical laws into the training process, allowing for efficient simulation of seismic phenomena, though they can be computationally intensive in high-dimensional scenarios. Quantum computing provides a transformative solution by leveraging entanglement and superposition to enhance computational efficiency. This study explores a hybrid quantum-classical PINN architecture, integrating a quantum layer into the network to reduce trainable parameters while evaluating its impact on performance. Results demonstrated that the hybrid model achieved comparable accuracy to the classical approach while significantly reducing the number of trainable parameters, highlighting its potential for efficient modeling. Future work should focus on improving the quantum ansatz and scaling this approach to tackle more complex geophysical challenges.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202539060
2025-03-24
2026-02-09
Loading full text...

Full text loading...

References

  1. Broggini, F. and Snieder, R. [2012] Connection of scattering principles: A visual and mathematical tour. European Journal of Physics, 33, 593–613.
    [Google Scholar]
  2. Cerezo, M., V.G.H.H.e.a. [2022] Challenges and opportunities in quantum machine learning. Nat Cornput Sci, 2, 567–576.
    [Google Scholar]
  3. Leao, C., Sethi, H., Koehne, V., Barrera, D. and Silva, P. [2024] A PINN Eikonal Equation Solution for the Marchenko Method First Arrivals. European Association of Geoscientists amp; Engineers, 2024(1), 1–5.
    [Google Scholar]
  4. Liu, C.Y., Kuo, E.J., Lin, C.H.A., Chen, S., Young, J.G., Chang, Y.J. and Hsieh, M.H. [2024] Training Classical Neural Networks by Quantum Machine Learning. arXiv.
    [Google Scholar]
  5. Raissi, M., Perdikaris, P. and Karniadakis, G. [2019] Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.
    [Google Scholar]
  6. Schuld, M. and Killoran, N. [2019] Quantum Machine Learning in Feature Hilbert Spaces. Phys. Rev. Lett., 122, 040504.
    [Google Scholar]
  7. Sethi, H., Pan, D., Pavel Dimitrov, J.S., Roth, G. and Hester, K. [2023] Hard enforcement of physics-informed neural network solutions of acoustic wave propagation. Computational & Geosciences, 27, 737–751.
    [Google Scholar]
  8. Trahan, C., Loveland, M. and Dent, S. [2024] Quantum Physics-Informed Neural Networks. Entropy, 26(8), 649.
    [Google Scholar]
  9. Zhao, Z., Ding, X. and Prakash, B.A. [2024] PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks.
    [Google Scholar]
/content/papers/10.3997/2214-4609.202539060
Loading
/content/papers/10.3997/2214-4609.202539060
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error