Experimentally Realizable Continuous-variable Quantum Neural Networks

The continuous-variable (CV) quantum computing has shown a great potential for building neural network models. These neural networks can have different levels of quantum-classical hybridization depending on the complexity of the problem. The previous work on CV neural network protocols requires the implementation of non-Gaussian operators in the network. These operators are used to introduce non-linearity which is an essential feature of neural networks. Due to these operators, the protocols become hard to execute experimentally. We built a CV hybrid quantum-classical neural network protocol that can be realized experimentally. Our protocol uses Gaussian gates only with the addition of ancillary qumodes. We achieve non-linearity through measurements on ancillary qumodes. Our gates can be implemented with squeezers and beam splitters; hence our protocol can be realized experimentally. We study two canonical machine learning problems in a supervised learning setting - curve fitting and binary classification to test our neural network. For the curve fitting problem, we used a fully quantum neural network which produced exceptional results as discussed in the paper. The second problem was more complicated in that we found the fraudulent transaction in the credit card data. After training, our model was able to identify genuine data with almost 92% accuracy. After studying both the problem, we can confidently conclude that our protocol gives good results with a great advantage that it can be implemented experimentally with current photonic quantum hardware.

Automated summary

The study explores the potential of CV quantum computing in building neural network models and proposes a hybrid quantum-classical neural network protocol that can be experimentally implemented. By using Gaussian gates and ancillary qumodes to achieve nonlinearity, the protocol overcomes the experimental difficulties and successfully addresses machine learning problems like curve fitting and binary classification.