Quantum channel discrimination presents a fundamental task in quantum information theory, with critical applications in quantum reading, illumination, data readout, and more. The extension to multiple quantum channel discrimination has seen a recent focus to characterize potential quantum advantage associated with quantum-enhanced discriminatory protocols. In this paper, we study thermal imaging as an environment localization task, in which thermal images are modeled as ensembles of Gaussian phase insensitive channels with identical transmissivity, and pixels possess properties according to background (cold) or target (warm) thermal channels. Via the teleportation stretching of adaptive quantum protocols, we derive ultimate limits on the precision of pattern classification of abstract, binary thermal image spaces, and show that quantum-enhanced strategies may be used to provide significant quantum advantage over known optimal classical strategies. The environmental conditions and necessary resources for which advantage may be obtained are studied and discussed. We then numerically investigate the use of quantum-enhanced statistical classifiers, where quantum sensors are used in conjunction with machine-learning image classification methods. Proving definitive advantage in the low-loss regime, this work motivates the use of quantum-enhanced sources for short-range thermal imaging and detection techniques for future quantum technologies.

中文翻译：

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热模式识别的极限

量子通道识别是量子信息理论中的一项基本任务，在量子读取，照明，数据读取等领域有着至关重要的应用。扩展到多个量子通道判别的方法近来已成为表征与量子增强的鉴别协议相关的潜在量子优势的焦点。在本文中，我们将热成像作为环境定位任务进行研究，其中热图像建模为具有相同透射率的高斯相位不敏感通道的集合，并且像素具有根据背景（冷）或目标（暖）热通道的属性。通过自适应量子协议的隐形传态扩展，我们得出了抽象，二进制热像空间的模式分类精度的最终极限，并表明，与已知的最佳经典策略相比，量子增强策略可用于提供显着的量子优势。研究和讨论了可以获取优势的环境条件和必要资源。然后，我们对使用量子增强型统计分类器进行数值研究，其中量子传感器与机器学习图像分类方法结合使用。在低损耗领域证明了绝对优势，这项工作激励了将量子增强源用于未来的量子技术的短程热成像和检测技术。然后，我们对使用量子增强型统计分类器进行数值研究，其中将量子传感器与机器学习图像分类方法结合使用。在低损耗领域证明了绝对优势，这项工作激励了将量子增强源用于未来的量子技术的短程热成像和检测技术。然后，我们对使用量子增强型统计分类器进行数值研究，其中将量子传感器与机器学习图像分类方法结合使用。在低损耗领域证明了绝对优势，这项工作激励了将量子增强源用于未来的量子技术的短程热成像和检测技术。