In this talk, we introduce the Trainable Embedding Quantum Physics-Informed Neural Network (TE-QPINN), a novel hybrid quantum–classical framework for solving nonlinear partial differential equations (PDEs). Our approach combines quantum machine learning with physics-informed neural networks (PINNs) to design algorithms that are compatible with noisy intermediate-scale quantum (NISQ) devices. A key ingredient of TE-QPINNs is the use of classical feedforward neural networks as problem-agnostic embedding functions, which map inputs to quantum circuit parameters. This design significantly enhances the expressivity of the underlying quantum model compared to previously proposed embedding schemes and removes the need for problem-specific ansätze. We further develop a hybrid backpropagation algorithm enabling efficient training of both the classical embedding networks and the quantum circuit parameters. We demonstrate the capabilities of TE-QPINNs on a range of benchmark problems, including the two-dimensional Poisson, Burgers, and Navier–Stokes equations. In direct comparison with classical PINNs, TE-QPINNs achieve superior accuracy using the same number of trainable parameters. These results indicate a potential for more efficient optimization in high-dimensional parameter spaces, and highlight the promise of hybrid quantum–classical physics-informed methods for future large-scale PDE applications on quantum hardware.