Quantum imaging using photon pairs with strong quantum correlations has been harnessed to bring quantum advantages to various fields from biological imaging to range finding. Such inherent non-classical properties support the extraction of more valid signals to build photon-limited images, even in low-light conditions where the shot noise becomes dominant as light decreases to a single-photon level. Numerical optimization algorithms are possible but require thousands of photon-sparse frames, and they are thus unavailable in real time. We demonstrate fast correlated-photon imaging enhanced by deep learning as an intelligent computational strategy to discover a deeper structure in big data. Our work verifies that a convolutional neural network can efficiently solve inverse imaging problems associated with strong shot noise and background noise (electronic noise, scattered light). Our results show that we can overcome limitations due to the trade-off between imaging speed and image quality by pushing the low-light imaging technique to the single-photon level in real time, which enables deep-learning-enhanced quantum imaging for real-life applications.

中文翻译：

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深度学习增强了快速相关光子成像

利用具有强量子相关性的光子对进行量子成像，已将量子优势带到了从生物成像到测距的各个领域。这种固有的非经典特性支持提取更多有效信号，以构建光子受限的图像，即使在光线不足时，当光降至单光子水平时散粒噪声占主导地位的弱光条件下。数值优化算法是可行的，但需要成千上万个光子稀疏帧，因此无法实时使用。我们演示了通过深度学习增强的快速相关光子成像，这是一种智能计算策略，可发现大数据中的更深结构。我们的工作验证了卷积神经网络可以有效解决与强散粒噪声和背景噪声（电子噪声，散射光）相关的逆成像问题。我们的结果表明，通过将微光成像技术实时推向单光子水平，我们可以克服由于成像速度和图像质量之间的折衷所带来的局限性，从而可以实现深度学习增强型量子成像，从而实现生活应用。