Physics-informed neural networks (PINNs) are widely recognised as powerful tools for solving partial differential equations (PDEs) by embedding physical laws directly into neural network architectures. Yet, classical PINN models often require a very large number of parameters to reach acceptable accuracy, making them computationally demanding especially for complex PDEs.

To address this challenge, Afrah Farea, a PhD candidate has introduced the Quantum-Classical Physics-Informed Neural Network (QCPINN), a framework that integrates quantum and classical computing components. This approach enables the solution of PDEs with significantly fewer parameters, while maintaining accuracy and convergence rates comparable to classical PINNs.

The full implementation is available open-source on GitHub.