GAN decoder on a quantum toric code for noise-robust quantum teleportation

TL;DR

This paper introduces a GAN-based decoder for quantum topological codes that significantly improves noise threshold and fidelity in quantum teleportation, demonstrating the potential of machine learning in quantum error correction.

Contribution

The paper presents a novel GAN decoder for quantum codes that outperforms classical decoders in noise threshold and fidelity, enhancing quantum teleportation protocols.

Findings

Achieves a pseudo-threshold of ~0.2108, nearly double the classical decoder.

Improves quantum teleportation fidelity within specific noise regimes.

Demonstrates GAN decoder's ability to generalize to other error models.

Abstract

We propose a generative adversarial network (GAN)-based decoder for quantum topological codes and apply it to enhance a quantum teleportation protocol under depolarizing noise. By constructing and training the GAN's generator and discriminator networks using eigenvalue datasets from the code, we obtain a decoder with a significantly improved decoding pseudo-threshold. Simulation results show that our GAN decoder achieves a pseudo-threshold of approximately $p=0.2108$, estimated from the crossing point of logical error rate curves for code distances $d=3$ and $d=5$, nearly double that of a classical decoder under the same conditions ($p \approx 0.1099$). Moreover, at the same target logical error rate, the GAN decoder consistently achieves higher logical fidelity compared to the classical decoder. When applied to quantum teleportation, the protocol optimized using our decoder demonstrates enhanced fidelity across noise regimes. Specifically, for code distance $d=3$, fidelity improves within the depolarizing noise threshold range $P<0.06503$; for $d=5$, the range extends to $P<0.07512$. Moreover, with appropriate training, our GAN decoder can generalize to other error models. This work positions GANs as powerful tools for decoding in topological quantum error correction, offering a flexible and noise-resilient framework for fault-tolerant quantum information processing.