Mapping and measuring large-scale photonic correlation with single-photon imaging
Ke Sun, Jun Gao, Ming-Ming Cao, Zhi‐Qiang Jiao, Yu Liu, Zhanming Li, Eilon Poem, A. Eckstein, Ruo-Jing Ren, Xiao-Ling Pang, Hao Long Tang, Ian A. Walmsley, Xian‐Min Jin
Quantum correlation and its measurement are essential in exploring fundamental quantum physics problems and developing quantum-enhanced technologies. A quantum correlation may be generated and manipulated in different spaces, which demands different measurement approaches corresponding to the position, time, frequency, and polarization of quantum particles. In addition, after early proof-of-principle demonstrations, it is of great demand to measure quantum correlation in a Hilbert space large enough for real quantum applications. When the number of modes goes up to several hundreds, the single-mode addressing becomes economically unfeasible, and the processing of correlation events with hardware also becomes extremely challenging. Here, we present a general and large-scale measurement approach of the Correlation on the Spatially Mapped Photon-Level Image. The quantum correlations in other spaces are mapped into the position space and are captured by a single-photon-sensitive imaging system. Synthetic methods are developed to suppress noises so that single-photon registrations can be faithfully identified in images. We eventually succeeded in retrieving all the correlations with a big-data technique from tens of millions of images.
Quantum correlation and its measurement are essential in exploring fundamental quantum physics problems and developing quantum-enhanced technologies. A quantum correlation may be generated and manipulated in different spaces, which demands different measurement approaches corresponding to the position, time, frequency, and polarization of quantum particles. In addition, after early proof-of-principle demonstrations, it is of great demand to measure quantum correlation in a Hilbert space large enough for real quantum applications. When the number of modes goes up to several hundreds, the single-mode addressing becomes economically unfeasible, and the processing of correlation events with hardware also becomes extremely challenging. Here, we present a general and large-scale measurement approach of the Correlation on the Spatially Mapped Photon-Level Image. The quantum correlations in other spaces are mapped into the position space and are captured by a single-photon-sensitive imaging system. Synthetic methods are developed to suppress noises so that single-photon registrations can be faithfully identified in images. We eventually succeeded in retrieving all the correlations with a big-data technique from tens of millions of images.
Quantum correlation and its measurement are essential in exploring fundamental quantum physics problems and developing quantum enhanced technologies. Quantum correlation may be generated and manipulated in different spaces, which demands different measurement approaches corresponding to position, time, frequency and polarization of quantum particles. In addition, after early proof-of-principle demonstrations, it is of great demand to measure quantum correlation in a Hilbert space large enough for real quantum applications. When the number of modes goes up to several hundreds, it becomes economically unfeasible for single-mode addressing and also extremely challenging for processing correlation events with hardware. Here we present a general and large-scale measurement approach of Correlation on Spatially-mapped Photon-Level Image (COSPLI). The quantum correlations in other spaces are mapped into the position space and are captured by single-photon-sensitive imaging system. Synthetic methods are developed to suppress noises so that single-photon registrations can be faithfully identified in images. We eventually succeed in retrieving all the correlations with big-data technique from tens of millions of images. We demonstrate our COSPLI by measuring the joint spectrum of parametric down-conversion photons. Our approach provides an elegant way to observe the evolution results of large-scale quantum systems, representing an innovative and powerful tool added into the platform for boosting quantum information processing.
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.
Cameras with single-photon sensitivities can be used to measure the spatial correlations between the photon-pairs that are produced by parametric down-conversion. Even when pumped by a single-mode laser, the signal and idler photons are typically distributed over several thousand spatial modes yet strongly correlated with each other in their position and anti-correlated in their transverse momentum. These spatial correlations enable applications in imaging, sensing, communication, and optical processing. Here we show that, using a photon-number resolving camera, spatial correlations can be observed after only a few 10s of seconds of measurement time, thereby demonstrating comparable performance with previous single photon sensitive camera technologies but with the additional capability to resolve photon-number. Consequently, these photon-number resolving technologies are likely to find wide use in quantum, low-light, imaging systems.
Quantum imaging leverages nonclassical correlations to reveal features that may vanish in conventional event integration [ 1, 2]. While single-photon detectors are well-established, quantum imaging demands complex data acquisition across many thousands of detectors, each generating rich metadata including triggering time, duration, and other properties. Consequently, effective data management, data wrangling, and correlation extraction become critical challenges. In this work, we investigate optimal handling of spatial correlations from a quantum-photonic source (spontaneous parametric down-conversion, SPDC) using an emerging single-photon event-based camera, the TPX3CAM (256×256 array, supported by an intensifier). Utilizing a femtosecond photon-pair source, we comprehensively analyse noise characteristics, including temporal dynamics and spatial distribution, to develop advanced spatio-temporal noise suppression strategies tailored to camera performance. Our primary objectives include real-time isolation and preprocessing of correlative data. Our analysis demonstrates the potential of these real-time correlation detection and noise reduction algorithms to expand quantum imaging capabilities across diverse scientific domains. Potential applications include high-resolution spectroscopy in biology, materials science, and advanced electron microscopy, where these techniques could enable identification of cascaded two-photon processes with atomic-scale precision. Figure 1 shows an excellent example of such a detection in real-space. The ring feature corresponds to the location of the correlated photon-pairs produced from the SPDC process. This is also apparent in figure 2, where we see a strong anti-correlation between the coordinates of the detected photons – corresponding to photon-pairs. <!-- named anchor --> Figure 1: Distribution of SPDC photons on TPX3CAM <!-- named anchor --> Figure 2: Spatial anti-correlation of x-coordinates
ABSTRACT Sources of light characterized by a well‐defined number of photons are widely used in quantum experiments and technologies due to their peculiar properties, which include sub‐Poissonian statistics, spatial, temporal, and frequency correlations, as well as maximal and high‐dimensional entanglement. Searching for the best experimental approaches that allow us to visualize the quantum state of two (or more) photons has been a long‐standing fundamental question, which has been mainly addressed via mode projection techniques. The rapid development of single‐photon‐sensitive cameras, however, has opened the pathway to conceptually simple, yet faster and more efficient, measurement techniques. This review explores the latest advancements in measuring the spatial structure of the quantum state of light using 3D imaging techniques. An overview of the most used single‐photon camera technologies is given, highlighting their differences and respective advantages. Besides the fundamental interest in reconstructing experimentally one of the most mysterious concepts of microscopic physics, this review illustrates how the techniques developed in this direction can lead to new ideas in the fields of imaging and sensing, for instance, superresolution measurements and phase‐enhanced and sub‐shot noise imaging.
Correlated photon pairs, carrying strong quantum correlations, have been harnessed to bring quantum advantages to various fields from biological imaging to range finding. Such inherent non-classical properties support extracting more valid signals to build photon-limited images even in low flux-level, where the shot noise becomes dominant as light source decreases to single-photon level. Optimization by numerical reconstruction algorithms is possible but require thousands of photon-sparse frames, thus unavailable in real time. Here, we present an experimental fast correlated-photon imaging enhanced by deep learning, showing an intelligent computational strategy to discover deeper structure in big data. Convolutional neural network is found being able to efficiently solve image inverse problems associated with strong shot noise and background noise (electronic noise, scattered light). Our results fill the key gap in incompatibility between imaging speed and image quality by pushing low-light imaging technique to the regime of real-time and single-photon level, opening up an avenue to deep leaning-enhanced quantum imaging for real-life applications.