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Neural networks can be used to represent quantum states. Here we explore andcompare different strategies for supervised learning of multilayer perceptrons.In particular, we consider two different loss functions which we refer to asmean-squared error and overlap, and we test their performance for the wavefunction in different phases of matter. For this, we consider the next-nearestneighbor Ising model because its ground state can be in various differentphases. Of these phases, we focus on the paramagnetic, ferromagnetic, andpair-antiferromagnetic phases, while for the training we study the role ofbatch size, number of samples, and size of the neural network. We observe thatthe overlap loss function allows us to train the model better across allphases, provided one rescales the neural network.

Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model