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  • Letter
  • Open Access

Quantum convolutional neural network for phase recognition in two dimensions

Leon C. Sander1,*, Nathan A. McMahon1,2,3, Petr Zapletal1,4, and Michael J. Hartmann1

  • *Contact author: leon.sander@fau.de
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Phys. Rev. Research 7, L042032 – Published 7 November, 2025

DOI: https://doi.org/10.1103/vpyw-nszk

Abstract

Quantum convolutional neural networks (QCNNs) are quantum circuits for characterizing complex quantum states. They have been proposed 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. Furthermore, it captures correlations between all stabilizer elements of the toric code, which cannot be accessed by direct measurements. This increases the threshold for errors leading to such correlations and allows for correctly identifying the topological phase in the presence of strong correlated errors. Our work generalizes phase recognition with QCNNs to higher spatial dimensions and intrinsic topological order, where exploration and characterization via classical numerics become challenging.

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Phys. Rev. Research 7, L042032– Published 7 November, 2025
  • Received 17 July 2024
  • Accepted 16 September 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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