利用量子退火的合成孔径雷达图像分割
Synthetic Aperture Radar Image Segmentation with Quantum Annealing
AI总结：
本文针对建模为马尔可夫随机场的合成孔径雷达图像分割的NP困难问题，提出一种混合量子退火与经典优化的期望最大化算法，并在D-Wave量子计算机上验证了其性能、可扩展性及绝热量子计算的潜力。
摘要：
在图像处理中，图像分割是将数字图像划分为多个图像区域的过程。在最先进的方法中，马尔可夫随机场（MRF）可用于对像素间的依赖关系进行建模，并通过最小化相关的代价函数来实现分割。目前，对于建模为 MRF 的给定图像，寻找最优的区域集合似乎是 NP 困难的。在本文中，我们旨在利用量子计算的指数级可扩展性来加速合成孔径雷达图像的分割。为此，我们提出了一种混合量子退火经典优化期望最大化算法，以获得最优的区域集合。在提出合适的公式化表述后，我们在 D-Wave 量子计算机上讨论了该方法的性能和可扩展性。我们还对最优计算参数进行了简要研究，以阐明绝热量子计算在解决组合优化问题大规模实例时的局限性和潜力。
英文摘要：
In image processing, image segmentation is the process of partitioning a digital image into multiple image segment. Among state-of-the-art methods, Markov Random Fields (MRF) can be used to model dependencies between pixels, and achieve a segmentation by minimizing an associated cost function. Currently, finding the optimal set of segments for a given image modeled as a MRF appears to be NP-hard. In this paper, we aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of Synthetic Aperture Radar images. For that purpose, we propose an hybrid quantum annealing classical optimization Expectation Maximization algorithm to obtain optimal sets of segments. After proposing suitable formulations, we discuss the performances and the scalability of our approach on the D-Wave quantum computer. We also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimization problems.
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