Quantum entanglement has emerged as a great resource for spectroscopy and its importance in two-photon spectrum and microscopy has been demonstrated. Current studies focus on the two-photon absorption, whereas the Raman spectroscopy with quantum entanglement still remains elusive, with outstanding issues of temporal and spectral resolutions. Here we study the new capabilities provided by entangled photons in coherent Raman spectroscopy. An ultrafast frequency-resolved Raman spectroscopy with entangled photons is developed for condensed-phase molecules, to probe the electronic and vibrational coherences. Using quantum correlation between the photons, the signal shows the capability of both temporal and spectral resolutions not accessible by either classical pulses or the fields without entanglement. We develop a microscopic theory for this Raman spectroscopy, revealing the electronic coherence dynamics even at timescale of 50fs. This suggests new paradigms of optical signals and spectroscopy, with potential to push detection below standard quantum limit.

We propose a computational ghost imaging (CGI) scheme using customized pink noise speckle pattern illumination. By modulating the power spectrum distribution of the speckles, we generate speckle patterns with a significant positive spatial intensity fluctuation correlation. We experimentally reconstruct images using our synthesized speckle patterns in the presence of a variety of noise sources and pattern distortion and show it is robust to noise environment. The results are compared with the use of standard white noise speckle patterns. We show that our method gives much better image qualities under different types of noise than the traditional way. The proposed scheme promises potential applications in underwater, dynamic, and moving target CGI.

Lateral flow assay (LFA) has long been used as a biomarker detection technique. It has advantages such as low cost, rapid readout, portability, and ease of use. However, its qualitative readout process and lack of sensitivity are limiting factors. We report a photon-counting approach to accurately quantify LFAs while enhancing sensitivity. In particular, we demonstrate that the density of SARS-CoV-2 antibodies can be quantified and measured with an enhanced sensitivity using this simple laser optical analysis.

Optical imaging through scattering media has long been a challenge. Many approaches have been developed for focusing light or imaging objects through scattering media, but usually, they are either invasive, limited to stationary or slow-moving media, or require high-resolution cameras and complex algorithms to retrieve the images. By utilizing spatial–temporal encoded patterns (STEPs), we introduce a technique for the computation of imaging that overcomes these restrictions. With a single-pixel photodetector, we demonstrate non-invasive imaging through scattering media. This technique is insensitive to the motion of the media. Furthermore, we demonstrate that our image reconstruction algorithm is much more efficient than correlation-based algorithms for single-pixel imaging, which may allow fast imaging for applications with limited computing resources.

We wish to report a recent experimental observation of anticorrelation from first-order and second-order incoherent thermal fields in the joint photodetection of two independent photodetectors. In the view of classical theory, the nontrivial second-order correlation of thermal light is caused by the statistical correlation of intensity fluctuations, which can be observed only from first-order coherent thermal radiations. What is the physical cause of this observed anticorrelation, then?

Artificial intelligence has recently been widely used in computational imaging. The deep neural network (DNN) improves the signal-to-noise ratio of the retrieved images, whose quality is otherwise corrupted due to the low sampling ratio or noisy environments. This work proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network. The simulation database assists the network in achieving signal translation ability. The experimental single-pixel detector’s signal will be ‘translated’ into a 2D image in an end-to-end manner. High-quality images with no background noise can be retrieved at a sampling ratio as low as 2%. The illumination patterns can be either well-designed speckle patterns for sub-Nyquist imaging or random speckle patterns. Moreover, our method is robust to noise interference. This translation mechanism opens a new direction for DNN-assisted ghost imaging and can be used in various computational imaging scenarios.