Quantum Convolutional Neural Network for Phase Recognition in Two Dimensions

ORAL

Abstract

Quantum convolutional neural networks (QCNNs) are quantum circuits for recognizing quantum phases of matter at low sampling cost and have been designed for condensed matter systems in one dimension. Here we construct a QCNN that can perform phase recognition in two dimensions and correctly identify the phase transition from a Toric Code phase with Z2-topological order to the paramagnetic phase. The network also exhibits a noise threshold up to which the topological order is recognized. Our work generalizes phase recognition with QCNNs to higher spatial dimensions and intrinsic topological order, where exploration and characterization via classical numerics become challenging.

*This work is part of the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus. Furthermore, this work was supported by the EU program HORIZON-MSCA-2022-PF project 101108476 HyNNet NISQ (PZ) and by the Alexander von Humboldt Foundation (NAM).

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Publication: https://arxiv.org/abs/2407.04114

Presenters

  • Petr Zapletal

    • University of Basel

Authors

  • Leon C Sander

    • FAU Erlangen-Nürnberg

  • Nathan A McMahon

    • Leiden University

  • Petr Zapletal

    • University of Basel

  • Michael Josef Hartmann

    • Friedrich-Alexander University Erlangen-Nuremberg