Reliable methods for the classification and quantification of quantum entanglement are fundamental to understanding its exploitation in technologies. One such method, known as Separable Neural Network Quantum States (SNNS), employs a neural network inspired parameterisation states whose properties explicitly programmable. Combined with generative machine learning methods, this ansatz allows stu...

We provide an adaptive learning algorithm for tomography of general quantum states. Our proposal is based on the simultaneous perturbation stochastic approximation and applies to mixed qudit The salient features our are efficient post-processing in dimension d state, robustness against measurement channel noise, improved infidelity performance as compared contemporary state algorithms. A higher...