发表机构
IonQ Inc.; Capella Space（IonQ公司; Capella空间公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将量子电路Born机（QCBM）生成模型用于SAR与InSAR变化检测，在稀疏图像场景性能优于经典方法，在机场SAR数据集获0.32滤波F1分，在火山InSAR数据集达约0.66，验证了离子阱硬件上该方法的可行性。
AI 中文摘要
合成孔径雷达（SAR）与干涉合成孔径雷达（InSAR）的变化检测是灾害响应、基础设施监测及土地用途管控的核心支撑。传统检测方法受限于背景估计器，该估计器通常直接基于观测像素统计量构建条件期望，在统计量稀疏的场景（包括亚米级雷达产生的重尾边缘分布场景）中性能会下降。本研究将最先进的卫星图像与基于IonQ离子阱量子处理器的量子机器学习相结合，通过在Copula空间中用量子电路Born机（QCBM）采样的生成模型替代经验条件，大幅提升了稀疏真实图像的变化检测性能。在Capella Space卫星图像采集数据中，当观测统计量充足时，该生成估计器与传统方法性能相当；当统计量不足时，其性能大幅优于传统方法。在IonQ离子阱硬件上执行训练后的模型，可复现理想及含噪声模拟的结果，且性能与经典最先进方法相当甚至更优。对于某机场的SAR数据集，训练与推理的QPU电路评估实现了0.32的最大滤波F1分数，而两个经典基线的F1分数分别为0.16和0.24；对于某火山熔岩流的InSAR数据集，三种方法均达到约0.66的最大滤波F1分数。这些实验证明了在离子阱硬件上执行基于QCBM的背景估计器的可行性，且QCBM方法可成功扩展到干涉相干性数据，实现与经典方法相当的性能。
英文摘要
Change detection in synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) underpins disaster response, infrastructure monitoring and land-use enforcement. Detection is limited by the background estimator, which conventionally forms a conditional expectation directly from observed pixel statistics and degrades where those statistics are sparse, including the regime produced by the heavy-tailed marginals of sub-meter-resolution radars. In this work, we integrate state-of-the-art satellite imagery with quantum machine learning on IonQ trapped-ion-based quantum processors. By replacing the empirical conditional with a quantum circuit Born machine (QCBM)-sampled generative model in Copula space, we substantially improve change detection on sparse real-world images. On Capella Space satellite image acquisitions, the generative estimator matches conventional methods when the observed statistics are adequate, and substantially outperforms them when they are not. Executing the trained model on IonQ trapped-ion based hardware reproduces the results of the ideal and noisy simulations and demonstrates up to par, or even better, performance with the classical state-of-the-art methods. For a SAR dataset of an airport, QPU circuit evaluations for both training and inference achieved a maximized filtered F1 score of 0.32, compared with 0.16 and 0.24 for the two classical baselines. For an InSAR dataset of a volcanic lava flow, all three methods reached a maximum filtered F1 of approximately 0.66. These experiments demonstrate the feasibility of executing a QCBM-based background estimator on trapped-ion hardware. We further demonstrate that the QCBM method successfully extends to interferometric coherence data, achieving performance comparable to classical approaches.
Comments16 pages, 4 figures, and 2 tables
