Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informed neural networks (PINNs) and their domain-decomposed variants (FBPINNs) offer a mesh-free alternative but face convergence challenges when representing complex velocity fields. We present a hybrid quantum-classical FBPINN for acoustic FWI, bringing together quantum computing and classical machine learning, in which the decomposed wavefield network and the global velocity network are implemented as classical-to-quantum pipelines terminating in parameterized quantum circuits (PQCs). The PQCs are realized as differentiable JAX statevector simulators, enabling end-to-end automatic differentiation through the classical PINN, the quantum circuit, and the physics-informed loss. On a geophysical anomaly benchmark, the quantum hybrid reaches a lower L1 velocity error than the primary classical FBPINN baseline in approximately 8x fewer training iterations, despite using approximately 33% fewer trainable parameters, and it outperforms all 15 classical hyperparameter variants tested. A second benchmark (checkerboard) demonstrates the generality of the inversion pipeline, confirming that the quantum hybrid architecture can recover structured spatial variations beyond the localized anomaly benchmark. Our framework is broadly applicable to wave-based inverse problems beyond geophysics, including medical ultrasound tomography and non-destructive evaluation.

翻译：全波形反演（FWI）通过接收器数据重建非均匀材料属性，但计算成本高昂。物理信息神经网络（PINNs）及其区域分解变体（FBPINNs）提供了一种无网格替代方案，但在表示复杂速度场时面临收敛挑战。本文提出一种用于声波FWI的混合量子-经典FBPINN，融合量子计算与经典机器学习，其中分解波场网络和全局速度网络被实现为以参数化量子电路（PQC）为终端的经典到量子流水线。这些PQC通过可微分的JAX状态向量模拟器实现，支持在经典PINN、量子电路和物理信息损失函数间进行端到端自动微分。在地球物理异常基准测试中，尽管量子混合模型的可训练参数比经典FBPINN基线少约33%，但其在约八分之一训练迭代次数内实现了更低的L1速度误差，并优于所有15种经典超参数变体。第二个基准测试（棋盘格）验证了反演流水线的通用性，证实量子混合架构能够恢复超越局部异常基准的结构化空间变化。我们的框架可广泛适用于地球物理之外的波动反演问题，包括医学超声层析成像和无损评估。