Observation of a Photonic Orbital Gauge Field.

Gauge field is widely studied in natural and artificial materials. With an effective magnetic field for uncharged particles, many intriguing phenomena are observed in several systems like photonic Floquet topological insulator. However, previous researches about the gauge field mostly focus on limited dimensions such as the Dirac spinor in graphene materials. Here, an orbital gauge field based on photonic triangular lattices is first proposed and experimentally observed. Disclination defects with Frank angle Ω created on such lattices breaks the original lattice symmetry and generates purely geometric gauge field operating on orbital basis functions. Interestingly, it is found that bound states near zero energy with the orbital angular momentum (OAM) l = 2 are intensively confined at the disclination as gradually expanding Ω. Moreover, the introduction of a vector potential field breaks the time-reversal symmetry of the orbital gauge field, experimentally manifested by the chiral transmission of light on helical waveguides. The orbital gauge field further suggests fantastic applications of manipulating the vortex light in photonic integrated devices.

Classification of quantum correlation using deep learning.

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.

Thresholded single-photon underwater imaging and detection.

Optical underwater target imaging and detection have been a tough but significant challenge in deep-sea exploration. Distant reflected signals drown in various underwater noises due to strong absorption and scattering, resulting in degraded image contrast and reduced detection range. Single-photon feature operating at the fundamental limit of the classical electromagnetic waves can broaden the realm of quantum technologies. Here we experimentally demonstrate a thresholded single-photon imaging and detection scheme to extract photon signals from the noisy underwater environment. We reconstruct the images obtained in a high-loss underwater environment by using photon-limited computational algorithms. Furthermore, we achieve a capability of underwater detection down to 0.8 photons per pulse at Jerlov type III water up to 50 meters, which is equivalent to more than 9 attenuation lengths. The results break the limits of classical underwater imaging and detection and may lead to many quantum-enhanced applications, like air-to-sea target tracking and deep-sea optical exploration.