发表机构
IonQ Team, App Dev Club, University of Maryland, College Park（离子量子团队、应用开发俱乐部、马里兰大学帕克分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对卫星图像野火分割难题，基于 U 型网络构建量子混合模型，在瓶颈部分注入变分量子电路，对比经典 FPN 等方法。实验表明量子增强方法及数据混合有优势，验证了架构对野火检测的有效性和通用性。
AI 中文摘要
从卫星图像中检测野火是一个语义图像分割问题，由于类别不平衡、特征复杂和大气干扰等挑战而变得困难。本文基于基础的 U 型网络图像分割模型开发了一种量子混合解决方案，以更有效地对 Sen2Fire 数据集的高维光谱特征空间进行建模。在 U 型网络的瓶颈部分注入变分量子电路，具体为 QuFeX 和 QB - Net 假设。测试了经典的特征金字塔网络（FPN）进行模型比较分析，还探索了对 U 型网络模型及其训练过程的经典改进，包括参数压缩、替代损失函数和输入数据的均匀混合。主要发现是在匹配条件下，QB - Net（F1 分数为 31.18）和 QuFeX（F1 = 30.79）优于经典 U 型网络基线结果（F1 = 28.71），经典 FPN 得分为 31.13。数据混合消除了地理上分离的数据集中显著的域偏移，将经典 FPN 的 F1 分数提高到 39.76。通过在加利福尼亚燃烧区域（CaBuAr）数据集上的跨数据集转移验证了该架构对野火检测问题的鲁棒性和通用性。总体而言，发现量子机器学习在野火图像分割问题中具有提供优势的潜力，进一步实验将继续验证和扩展这一发现。
英文摘要
Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference. In this paper, we build on the foundational U-Net image segmentation model to develop a quantum-hybrid solution in hopes of more effectively modeling the high-dimensional spectral feature space of the Sen2Fire dataset. We inject a variational quantum circuit in the bottleneck portion of U-Net, specifically the QuFeX and QB-Net ansatzes. We test a classical Feature Pyramid Network (FPN) for further comparative analysis of the model, and we also explore classical improvements to the U-Net model and its training process, including a compression of parameters, alternative loss functions, and uniform mixing of input data. Our primary finding is that under matched conditions, both QB-Net (with an $F_1$ score of 31.18) and QuFeX ($F_1 = 30.79$) outperformed the classical U-Net baseline results ($F_1 = 28.71$). Additionally, the classical FPN achieved a comparable score of 31.13. A crucial finding was that data mixing removed a significant domain shift between the geographically-separated train and test sets, which boosted the classical FPN $F_1$ score to 39.76. We validate the architecture's robustness and generalizability to the wildfire detection problem via cross-dataset transfer on the California Burned Areas (CaBuAr) dataset. Overall, we find that quantum machine learning has potential to provide an advantage in the problem of wildfire image segmentation, and further experiments will continue to validate and expand upon this finding.
Comments19 pages, 8 figures
