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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.