Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network
Abstract
Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.
Cite
@article{arxiv.2310.05900,
title = {Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network},
author = {Johannes Bausch and Andrew W Senior and Francisco J H Heras and Thomas Edlich and Alex Davies and Michael Newman and Cody Jones and Kevin Satzinger and Murphy Yuezhen Niu and Sam Blackwell and George Holland and Dvir Kafri and Juan Atalaya and Craig Gidney and Demis Hassabis and Sergio Boixo and Hartmut Neven and Pushmeet Kohli},
journal= {arXiv preprint arXiv:2310.05900},
year = {2024}
}