Fig. 1: Hardware-efficient quantum machine learning enhanced ghost imaging.
From: Practical advantage of quantum machine learning in ghost imaging
a The typical experimental setup for ghost imaging in which the patterns can be randomly sampled or optimized according to the poster processing and a single-pixel detector is used to measure the object-interacted light field modulated by the prescribed illumination patterns. The patterns and bucket signals are correlated to recover the image. b Hybrid quantum machine learning algorithm by combining the artificial neural network or convolution neural network enhances object identification and the quality of object imaging. Quantum feature encoding and learning operations are hardware-efficient such that they can be implemented in the noisy intermediate-scale quantum (NISQ) devices.
