Model Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks

Title: Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
Speaker: Yu-Jie Liu from Technical University of Munich/ Munich Center for Quantum Science and Technology
Abstract:
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 wave functions of the quantum phase and 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 toward hardware-efficient training of quantum phase classifiers on a programmable quantum processor.
Paper: 10.1103/PhysRevLett.130.220603