Fig. 2: The identification results of ghost imaging (GI) system based on classical machine learning (CML) and hybrid quantum machine learning (QML) algorithm.
From: Practical advantage of quantum machine learning in ghost imaging
a The loss (left) and validation accuracy curve (right) of hybrid QML varied with the training epochs, where the bucket signals are measured based on the optimized patterns. b The ultimate identification precision of classical and hybrid (noisy, 10−3 or 3 × 10−3) QML with approximately equal size of parameter space varied with different numbers of optimized illumination patterns. c The t-distributed stochastic neighbor embedding (TSNE) visualization of raw bucket signals with random patterns (M = 16). d The loss (left) and validation accuracy (right) curve of hybrid QML varied with the training epochs based on random patterns. e The final accuracy of classical and hybrid (noisy, 10−3 or 3 × 10−3) QML for different number of random patterns. f The TSNE visualization of hybrid QML optimized bucket signals with random patterns (M = 16).
