.png){kind=icon}
返回
Classification of quantum correlation using deep learning
.png){kind=icon}
OA
AI
DOI：10.1364/OE.477046.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quantum correlation, as an intrinsic property of quantum mechanics, has been widely employed to test the fundamental physical principles and explore the quantum-enhanced technologies. However, such correlation would be drowned and even destroyed in the conditions of high levels of loss and noise, which drops into the classical realm and renders quantum advantage ineffective. Especially in low light conditions, conventional linear classifiers are unable to extract and distinguish quantum and classical correlations with high accuracy. Here we experimentally demonstrate the classification of quantum correlation using deep learning to meet the challenge in the quantum imaging scheme. We design the convolutional neural network to learn and classify the correlated photons efficiently with only 0.1 signal photons per pixel. We show that decreasing signal intensity further weakens the correlation and makes an accurate linear classification impossible, while the deep learning method has a strong robustness of such task with the accuracy of 99.99%. These results open up a new perspective to optimize the quantum correlation in low light conditions, representing a step towards diverse applications in quantum-enhanced measurement scenarios, such as super-resolution microscope, quantum illumination, etc.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Keyword:
ENTANGLEMENT
COMMUNICATION
SECURITY
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
分享收藏Absolute calibration of an EMCCD camera by quantum correlation, linking photon counting to the analog regime.png)
OPTICS LETTERS
OAAI
OPTICS LETTERSIF3.3
OAAI分享收藏
查看更多内容