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

Abstract

Precision experimental determination of photon correlation requires the 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 the $g^{(2)}(0)$ 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 seconds and achieving a three orders of magnitude acceleration in data acquisition time. This technique contributes to a high-speed and precision coherence evaluation of entropy source in secure communication and quantum imaging.

6 pages, 6 figures

References in corpus (6)

• Quantum computational advantage using photons
• 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
• Optical Quantum Computing
• Real time demonstration of high bitrate quantum random number generation with coherent laser light
• Experimental demonstration of separating the waveparticle duality of a single photon with the quantum Cheshire cat
• Chaotic time-delay signature suppression using quantum noise