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

• URL: http://arxiv.org/abs/2211.11786v3
• Date: Fri, 26 May 2023 22:46:36 GMT
• Title: Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
• Authors: Yu-Jie Liu, Adam Smith, Michael Knap, and Frank Pollmann
• Abstract summary: Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. We propose a model-independent protocol for training QCNNs to discover order parameters that are unchanged under phase-preserving perturbations.
• Score: 1.9404281424219032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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