Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
The QCPINN framework integrates classical networks with a Discrete Variable quantum core to solve complex reservoir seepage equations. It overcomes limitations of traditional methods and classical PINNs by using quantum circuits for high-dimensional feature mapping, achieving superior accuracy and efficiency across various reservoir models like pressure diffusion and Buckley-Leverett.