High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection

Precision experimental determination of photon correlation requires massive amounts of data and extensive measurement time. We present a technique to monitor second-order photon correlation g ( 2 ) ( 0 ) of amplified quantum noise based on wideband balanced homodyne detection and deep-learning acceleration. The quantum noise is effectively amplified by an injection of weak chaotic laser, and the g ( 2 ) ( 0 ) of the amplified quantum noise is measured with a real-time sample rate of 1.4 GHz. We also exploit a photon correlation convolutional neural network accelerating correlation data using a few quadrature fluctuations to perform a parallel processing of g ( 2 ) ( 0 ) for various chaos injection intensities and effective bandwidths. The deep-learning method accelerates the g ( 2 ) ( 0 ) experimental acquisition with a high accuracy, estimating 6107 sets of photon correlation data with a mean square error of 0.002 in 22 s and achieving a three orders of magnitude acceleration in the data acquisition time. This technique contributes to a high-speed and precision coherence evaluation of entropy source in secure communication and quantum imaging.