用量子电路实现超轻量肺部电阻抗成像重建，参数少至0.2%却更抗噪。
QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography
- 用两比特量子电路生成隐式先验，仅用线性层完成重建。
- 参数量仅为传统方法的0.2%，仍保持高精度。
- 无需训练数据，首次将量子电路用于EIT图像重建。
电阻抗断层成像（EIT）是一种无创、低成本、高时间分辨率的床边成像技术，适用于实时监测。然而其固有的不适定逆问题给准确图像重建带来挑战。深度学习方法虽有潜力，但常依赖复杂网络结构和大量参数，限制了效率与可扩展性。本文提出一种超轻量量子辅助推理框架QuantEIT，采用量子辅助网络（QA-Net），通过并行双量子比特电路生成表达性强的潜在表示，作为隐式非线性先验，再通过单个线性层实现电导率重建。该设计大幅降低模型复杂度与参数量。独特之处在于QuantEIT以无监督、无需训练数据的方式运行，是首个将量子电路引入EIT图像重建的工作。在模拟与真实世界2D/3D肺部EIT数据上的大量实验表明，QuantEIT在仅使用0.2%参数量的情况下，仍达到或超越传统方法的重建精度，并具备更强的抗噪能力。
原文摘要 · Abstract (English)
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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