Deep neural networks for quantum state characterization, part 1: classification

Neural-network-based machine-learning techniques are becoming ubiquitous in quantum information and computing. Some problems faced during characterization of quantum systems can be translated to machine-learning tasks where deep neural networks have proven successful in many domains. Many of these tasks are data-driven, e.g., identifying interesting properties of quantum systems, state discrimination, and tomography. We discuss the problem of quantum state characterization in the context of discriminative modelling. We show that deep-neural-network-based techniques can be adopted for quantum state classification and reconstruction under different types of noise, requiring fewer data points and converging faster than standard methods by using optical quantum states as examples. We demonstrate how convolutional neural networks can distinguish several classes of optical quantum states, including bosonic codes. We further present one possibility for adaptive data collection during tomography by analysing which data points a trained neural network considers important for state classification.