A quantum-assisted latent surrogate model for Bayesian inversion of heterogeneous material properties on NISQ hardware
Abstract
Bayesian inversion is widely used in engineering applications, including material characterisation, structural damage identification, and medical diagnostics. These inverse problems are typically ill-posed, because localised variations in material properties must be inferred from noisy observations, and they are computationally intensive, since they often require many evaluations of a high-fidelity forward solver. Quantum computing has recently attracted considerable interest, partly because some near-term algorithms are feasible on NISQ hardware. In particular, PQC combined with classical optimisation enable a hybrid quantum-classical surrogate model. A central obstacle to applying such circuits to inversion is that the discretised parameter and response fields are far too high-dimensional to be embedded in a near-term quantum register. In this work we propose a quantum-assisted latent surrogate model for the Bayesian inversion of inhomogeneous material properties, in which latent compression bridges these high-dimensional fields and the small registers accessible to near-term circuits. The low intrinsic dimensional structure of the spatially varying conductivity and potential fields is exploited by reducing the conductivity through PCA and the response through a convolutional autoencoder. A PQC is then trained to map the latent conductivity to the latent response, and stochastic variational inference is performed entirely in latent space. The methodology is demonstrated on one- and two-dimensional inverse problems under both informative and non-informative priors. We further evaluate the model on IonQ simulator and Rigetti Cepheus-1-108Q, and use a lightweight learned denoising stage that recovers the noiseless accuracy to within a small residual. (The abstract has been shortened to meet arXiv's 1,920-character limit. For the full Abstract, please download the full Article.)
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BibTeX
@misc{alfarisy2026quantumassisted,
title = {{A quantum-assisted latent surrogate model for Bayesian inversion of heterogeneous material properties on NISQ hardware}},
author = {Dewangga Alfarisy and Catherine F. Higham and Eky Febrianto},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.18942},
eprint = {2610.18942},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.18942},
}
RIS
TY - UNPB
TI - A quantum-assisted latent surrogate model for Bayesian inversion of heterogeneous material properties on NISQ hardware
AU - Dewangga Alfarisy
AU - Catherine F. Higham
AU - Eky Febrianto
PY - 2026
DA - 2026/10/13
PB - arXiv
DO - 10.48550/arxiv.2610.18942
UR - https://arxiv.org/abs/2610.18942
AB - Bayesian inversion is widely used in engineering applications, including material characterisation, structural damage identification, and medical diagnostics. These inverse problems are typically ill-posed, because localised variations in material properties must be inferred from noisy observations, and they are computationally intensive, since they often require many evaluations of a high-fidelity forward solver. Quantum computing has recently attracted considerable interest, partly because some near-term algorithms are feasible on NISQ hardware. In particular, PQC combined with classical optimisation enable a hybrid quantum-classical surrogate model. A central obstacle to applying such circuits to inversion is that the discretised parameter and response fields are far too high-dimensional to be embedded in a near-term quantum register. In this work we propose a quantum-assisted latent surrogate model for the Bayesian inversion of inhomogeneous material properties, in which latent compression bridges these high-dimensional fields and the small registers accessible to near-term circuits. The low intrinsic dimensional structure of the spatially varying conductivity and potential fields is exploited by reducing the conductivity through PCA and the response through a convolutional autoencoder. A PQC is then trained to map the latent conductivity to the latent response, and stochastic variational inference is performed entirely in latent space. The methodology is demonstrated on one- and two-dimensional inverse problems under both informative and non-informative priors. We further evaluate the model on IonQ simulator and Rigetti Cepheus-1-108Q, and use a lightweight learned denoising stage that recovers the noiseless accuracy to within a small residual. (The abstract has been shortened to meet arXiv's 1,920-character limit. For the full Abstract, please download the full Article.)
ER -