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Precision experimental determination of photon correlation requires themassive amounts of data and extensive measurement time. We present a techniqueto monitor second-order photon correlation $g^{(2)}(0)$ of amplified quantumnoise based on wideband balanced homodyne detection and deep-learningacceleration. The quantum noise is effectively amplified by an injection ofweak chaotic laser and the $g^{(2)}(0)$ of the amplified quantum noise ismeasured with a real-time sample rate of 1.4 GHz. We also exploit a photoncorrelation convolutional neural network accelerating correlation data using afew quadrature fluctuations to perform a parallel processing of the$g^{(2)}(0)$ for various chaos injection intensities and effective bandwidths.The deep-learning method accelerates the $g^{(2)}(0)$ experimental acquisitionwith a high accuracy, estimating 6107 sets of photon correlation data with amean square error of 0.002 in 22 seconds and achieving a three orders ofmagnitude acceleration in data acquisition time. This technique contributes toa high-speed and precision coherence evaluation of entropy source in securecommunication and quantum imaging.

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