Experimentally Realizable Continuous-variable Quantum Neural Networks

We build a continuous variable (CV) hybrid quantum-classical neural network protocol and show how CV supervised learning with hybrid networks can be used for fraud detection. Previous work on CV neural networks protocols for fraud detection required implementation of non-Gaussian operators which are hard to realize experimentally. Our protocol uses Gaussian gates only with the addition of ancillary qumodes. We achieve non-linearity, an essential feature of neural networks, through measurements on ancillary qumodes. Our gates can be implemented with squeezers and beam splitters; hence our protocol can be realized experimentally with current photonic quantum hardware.