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We demonstrate how to map out the phase diagram of a two dimensional quantummany body system with no prior physical knowledge by applying deep\textit{anomaly detection} to ground states from infinite projected entangledpair state simulations. As a benchmark, the phase diagram of the 2D frustratedbilayer Heisenberg model is analyzed, which exhibits a second-order and twofirst-order quantum phase transitions. We show that in order to get a goodqualitative picture of the transition lines, it suffices to use data from thecost-efficient simple update optimization. Results are further improved bypost-selecting ground-states based on their energy at the cost of contractingthe tensor network once. Moreover, we show that the mantra of ``more trainingdata leads to better results'' is not true for the learning task at hand andthat, in principle, one training example suffices for this learning task. Thisputs the necessity of neural network optimizations for these learning tasks inquestion and we show that, at least for the model and data at hand, a simplegeometric analysis suffices.

Unsupervised mapping of phase diagrams of 2D systems from infinite projected entangled-pair states via deep anomaly detection