Low-overhead quantum error correction codes with a cyclic topology

TL;DR

This paper introduces a resource-efficient quantum error correction code with cyclic topology tailored for superconducting qubit architectures, utilizing neural network decoding to exponentially suppress logical errors.

Contribution

It proposes a novel cyclic stabilizer code for small distances, incorporating neural network decoding supported by an improved lookup table, optimized for superconducting platforms.

Findings

Exponential suppression of logical error rate demonstrated.

Construction of quantum circuits with non-neighboring entangled ancillas.

Resource-efficient scaling of five-qubit perfect code with cyclic stabilizers.

Abstract

Quantum error correction is an important ingredient for scalable quantum computing. Stabilizer codes are one of the most promising and straightforward ways to correct quantum errors, are convenient for logical operations, and improve performance with increasing the number of qubits involved. Here, we propose a resource-efficient scaling of a five-qubit perfect code with increasing-weight cyclic stabilizers for small distances on the ring architecture, which takes into account the topological features of the superconducting platform. We show an approach to construct the quantum circuit of a correction code with ancillas entangled with non-neighboring data qubits. Furthermore, we introduce a neural network-based decoding algorithm supported by an improved lookup table decoder and provide a numerical simulation of the proposed code, which demonstrates the exponential suppression of the logical error rate.