Learning to Decode the Surface Code with a Recurrent, Transformer-Based
  Neural Network
Johannes Bausch Andrew W Senior Francisco J H Heras Thomas Edlich Alex Davies Michael Newman Cody Jones Kevin Satzinger Murphy Yuezhen Niu Sam Blackwell George Holland Dvir Kafri Juan Atalaya Craig Gidney Demis Hassabis Sergio Boixo Hartmut Neven Pushmeet Kohli
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.