Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network

• Main Authors: Bausch, Johannes, Senior, Andrew W, Heras, Francisco J H, Edlich, Thomas, Davies, Alex, Newman, Michael, Jones, Cody, Satzinger, Kevin, Niu, Murphy Yuezhen, Blackwell, Sam, Holland, George, Kafri, Dvir, Atalaya, Juan, Gidney, Craig, Hassabis, Demis, Boixo, Sergio, Neven, Hartmut, Kohli, Pushmeet
• Format: Journal Article
• Language: English
• Published: 09-10-2023
• Subjects: Computer Science - Learning; Physics - Quantum Physics
• DOI: 10.48550/arxiv.2310.05900

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.