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An efficient state estimation model, neural network estimation (NNE),empowered by machine learning techniques, is presented for full quantum statetomography (FQST). A parameterized function based on neural network is appliedto map the measurement outcomes to the estimated quantum states. Parameters areupdated with supervised learning procedures. From the computational complexityperspective our algorithm is the most efficient one among existing stateestimation algorithms for full quantum state tomography. We perform numericaltests to prove both the accuracy and scalability of our model.

Neural network state estimation for full quantum state tomography