  Quantum convolutional neural networks (QCNNs) have been introduced as
classifiers for gapped quantum phases of matter. Here, we propose a
model-independent protocol for training QCNNs to discover order parameters that
are unchanged under phase-preserving perturbations. We initiate the training
sequence with the fixed-point wavefunctions of the quantum phase and then add
translation-invariant noise that respects the symmetries of the system to mask
the fixed-point structure on short length scales. We illustrate this approach
by training the QCNN on phases protected by time-reversal symmetry in one
dimension, and test it on several time-reversal symmetric models exhibiting
trivial, symmetry-breaking, and symmetry-protected topological order. The QCNN
discovers a set of order parameters that identifies all three phases and
accurately predicts the location of the phase boundary. The proposed protocol
paves the way towards hardware-efficient training of quantum phase classifiers
on a programmable quantum processor.
