Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
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 simu...
| Authors | Hoang Anh Nguyen, Divakar Vashisth, Ali Tura |
|---|---|
| Primary category | physics.geo-ph |
| Published | 31 May 2026 |
| Last updated at source | 15 June 2026 |
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