Quantum-Assisted Trainable-Embedding Physics-Informed Neural Networks for Parabolic PDEs

TL;DR

This paper explores quantum-assisted physics-informed neural networks (PINNs) for solving parabolic PDEs, introducing two hybrid architectures that leverage quantum and classical embedding strategies to improve PDE modeling.

Contribution

It presents novel hybrid quantum-classical PINN architectures with trainable embeddings for solving heat equations, highlighting the importance of embedding design in quantum PDE solvers.

Findings

Hybrid quantum-classical PINNs are effective for parabolic PDEs.

Embedding design significantly impacts model performance.

Quantum embeddings can enhance PDE solution capabilities.

Abstract

Physics-informed neural networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding governing physical laws directly into the training objective. Recent advances in quantum machine learning have motivated hybrid quantum-classical extensions aimed at enhancing representational capacity while remaining compatible with near-term quantum hardware. In this work, we investigate trainable embedding strategies within quantum-assisted PINNs for solving parabolic PDEs, using one- and two-dimensional heat equations as canonical benchmarks. We introduce two quantum-assisted architectures that differ in their embedding components. In the first approach, a classical feed-forward neural network generates trainable feature maps for quantum data encoding (FNN-TE-QPINN). In the second, the embedding stage is realized entirely by a parameterized quantum circuit (QNN-TE-QPINN), yielding a fully quantum feature map. Our findings emphasize the critical role of embedding design and support hybrid quantum-classical approaches for parabolic PDE modeling in the NISQ era.