Neural-network quantum state tomography

We revisit the application of neural networks to quantum state tomography. confirm that positivity constraint can be successfully implemented with trained convert outputs from standard feed-forward valid descriptions states. Any neural-network architecture adapted our method. Our results open possibilities use state-of-the-art deep-learning methods for reconstruction under various types noise.