QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography

• URL: http://arxiv.org/abs/2507.14031v1
• Date: Fri, 18 Jul 2025 15:57:53 GMT
• Title: QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography
• Authors: Hao Fang, Sihao Teng, Hao Yu, Siyi Yuan, Huaiwu He, Zhe Liu, Yunjie Yang,
• Abstract summary: Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution.<n>Deep learning approaches have shown promise but often rely on complex network architectures with a large number of parameters.<n>We propose an Ultra-Lightweight Quantum-Assisted Inference framework for EIT image reconstruction.
• Score: 9.873236202827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution, making it suitable for bedside monitoring. However, its inherently ill-posed inverse problem poses significant challenges for accurate image reconstruction. Deep learning (DL)-based approaches have shown promise but often rely on complex network architectures with a large number of parameters, limiting efficiency and scalability. Here, we propose an Ultra-Lightweight Quantum-Assisted Inference (QuantEIT) framework for EIT image reconstruction. QuantEIT leverages a Quantum-Assisted Network (QA-Net), combining parallel 2-qubit quantum circuits to generate expressive latent representations that serve as implicit nonlinear priors, followed by a single linear layer for conductivity reconstruction. This design drastically reduces model complexity and parameter number. Uniquely, QuantEIT operates in an unsupervised, training-data-free manner and represents the first integration of quantum circuits into EIT image reconstruction. Extensive experiments on simulated and real-world 2D and 3D EIT lung imaging data demonstrate that QuantEIT outperforms conventional methods, achieving comparable or superior reconstruction accuracy using only 0.2% of the parameters, with enhanced robustness to noise.

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