  We present a binary classifier based on neural networks to detect gapped
quantum phases. By considering the errors on top of a suitable reference state
describing the gapped phase, we show that a neural network trained on the
errors can capture the correlation between the errors and can be used to detect
the phase boundaries of the gapped quantum phase. We demonstrate the
application of the method for matrix product state calculations for different
quantum phases exhibiting local symmetry-breaking order, symmetry-protected
topological order, and intrinsic topological order.
