Quantum Convolutional Neural Network for Phase Recognition in Two Dimensions

TL;DR

This paper introduces a quantum convolutional neural network capable of recognizing topological phases in two-dimensional quantum systems, effectively identifying phase transitions and maintaining robustness against correlated noise.

Contribution

It extends quantum convolutional neural networks to two-dimensional systems with topological order, enabling phase recognition and noise resilience beyond previous one-dimensional models.

Findings

Successfully identifies phase transition from Toric Code to paramagnetic phase.

Maintains noise threshold for topological order recognition.

Captures correlations inaccessible by direct measurements.

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 $\mathbb{Z}_2$-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.