Quantum convolutional neural network for phase recognition in two dimensions

Physical Review Research, vol. 7

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 Z 2 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.

Authors 4

1. Leon C. Sander

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Friedrich-Alexander University Erlangen-Nürnberg (FAU)

2. Nathan A. McMahon Aachen

   Leiden University · Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Friedrich-Alexander University Erlangen-Nürnberg (FAU)

   Leiden University

3. Petr Zapletal

   Friedrich-Alexander-Universität Erlangen-Nürnberg · University of Basel

   Affiliation as printed

   Friedrich-Alexander University Erlangen-Nürnberg (FAU)

   University of Basel

   Department of Physics, University of Basel, Basel, Switzerland

4. Michael J. Hartmann

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Friedrich-Alexander University Erlangen-Nürnberg (FAU)