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

TL;DR

This paper introduces a deep-learning enhanced balanced homodyne detection method for rapid, real-time measurement of photon correlation in amplified quantum noise, significantly reducing data acquisition time.

Contribution

It presents a novel combination of wideband balanced homodyne detection and neural networks to accelerate quantum noise photon correlation measurements.

Findings

Achieved real-time $g^{(2)}(0)$ measurement at 1.4 GHz sample rate.

Accelerated data acquisition by three orders of magnitude.

Estimated 6107 photon correlation datasets with high accuracy in 22 seconds.

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.