arXiv：cs.LG· Nuno Batista, Ana Morgado, Oscar Ferraz, Sagar Silva Pratapsi, Jorge Lobo, Gabriel Falcao·· 3 小时前
基于冷原子储备池计算与自编码器的医学影像分类
Medical Imaging Classification with Cold-Atom Reservoir Computing using Auto-Encoders and Surrogate-Driven Training
AI 导读
研究者提出一种基于中性原子储备池计算的混合量子-经典流水线，用于息肉检测的二分类医学影像任务。该方法将引导式自编码器学到的潜在表示编码为 Rydberg Hamiltonian 中的脉冲失谐参数，并通过可微代理模型绕过量子测量的梯度障碍，实现端到端反向传播。模拟显示其性能优于使用 PCA 或无引导自编码器的传统方法，并已在 2025 IEEE QAI 发表。
正文
Abstract:We introduce a hybrid quantum-classical pipeline, based on neutral-atom reservoir computing, for medical image classification, focusing on the binary classification task of polyp detection. To deal effectively with the high dimensionality, we integrate a guided auto-encoder. This pipeline learns compact and discriminative representations of image data that are also well-suited for quantum reservoir computing. A key challenge in such systems is the non-differentiable nature of quantum measurements, which creates a 'gradient barrier' for standard training. We overcome this barrier by incorporating a differentiable surrogate model that emulates the quantum layer, enabling end-to-end backpropagation through the entire system. This guided training process is jointly optimized for classification accuracy and for faithful image recovery from the auto-encoder. The learned latent representations are encoded as pulse detuning parameters within a Rydberg Hamiltonian, and quantum embeddings are subsequently obtained through expectation values. These embeddings are then passed to a linear classifier. Our simulations show that this method outperforms some traditional approaches that use PCA or unguided autoencoders. We also conduct ablation studies to assess the impact of various quantum and training parameters, demonstrating the robustness and flexibility of our proposed pipeline for real-world medical imaging applications, even in the current NISQ era.
| Comments: | 8 pages, 6 figures. Accepted to the 2025 IEEE International Conference on Quantum AI (IEEE QAI). Supported by FCT and the Open Quantum Institute (OQI). v2: corrects the stated Rabi frequency to $Ω= 2π$ rad/$μ$s |
| Subjects: | Machine Learning (cs.LG); Emerging Technologies (cs.ET); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2605.06727 [cs.LG] |
| (or arXiv:2605.06727v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06727 arXiv-issued DOI via DataCite |
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| Journal reference: | 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI) |
| Related DOI: | https://doi.org/10.1109/QAI63978.2025.00064
DOI(s) linking to related resources |
Submission history
From: Nuno Batista [view email]
[v1]
Thu, 7 May 2026 11:26:09 UTC (459 KB)
[v2]
Wed, 14 Oct 2026 15:02:17 UTC (459 KB)
来源：arXiv：cs.LG · arxiv.org