A Trainable-Embedding Quantum Physics-Informed Framework for Multi-Species Reaction-Diffusion Systems

Physics-informed neural networks (PINNs) and hybrid quantum-classical extensions provide a promising framework for solving partial differential equations (PDEs) by embedding physical laws directly into the learning process. In this work, we study embedding strategies for trainable embedding quantum physics-informed neural networks (TE-QPINNs) in the context of nonlinear reaction-diffusion (RD) systems. We introduce an extended TE-QPINN (x-TE-QPINN) architecture that supports both classical and fully quantum embeddings, enabling a controlled comparison between feedforward neural network-based feature maps and parameterized quantum circuit embeddings. The first architecture is the classical embedding feed-forward neural network-based TE-QPINN (FNN-TE-QPINN), while the latter variant is a purely quantum one, referred to as quantum embedding neural network-based TE-QPINN (QNN-TE-QPINN). The proposed framework employs hardware-efficient variational quantum circuits and species-specific readout operators to approximate coupled multi-field dynamics while enforcing governing equations, boundary conditions, and initial conditions through a physics-informed loss function. By isolating the embedding mechanism while keeping the variational ansatz, loss formulation, and optimization procedure fixed, we analyze the impact of embedding design on gradient structure, parameter scaling, and quantum resource requirements. Numerical experiments on one- and two-dimensional RD equations demonstrate that quantum embeddings can replace classical embeddings without degradation in solution accuracy and, in certain regimes, exhibit improved optimization behavior compared to classical PINNs and hybrid quantum models with fixed embeddings. These results provide architectural insight into hybrid quantum PDE solvers and inform the design of resource-efficient quantum physics-informed learning methods.

💡 Research Summary

The paper presents a novel architecture for solving coupled nonlinear reaction‑diffusion (RD) systems by integrating quantum embeddings into physics‑informed neural networks (PINNs). Building on prior work that combined parameterized quantum circuits (PQCs) with PINNs (so‑called QPINNs), the authors focus on the embedding stage—the mechanism that maps classical spatiotemporal coordinates (x, t) into quantum states. Traditionally, this embedding has been implemented with a classical feed‑forward neural network (FNN). The authors propose to replace the FNN with a fully quantum neural network (QNN), yielding a Trainable‑Embedding Quantum PINN (TE‑QPINN) that can operate with either a classical embedding (FNN‑TE‑QPINN) or a quantum embedding (QNN‑TE‑QPINN) while keeping the downstream variational circuit, loss formulation, and optimization pipeline identical.

Key technical components include: (1) Normalization of inputs to the interval