Learning High-Accuracy Error Decoding for Quantum Processors
Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical quantum systems.
Quantum error-correction codes present a way to reach this goal by encoding logical information redundantly into many physical qubits. A key challenge in implementing such codes is accurately decoding noisy syndrome information extracted from redundancy checks to obtain the correct encoded logical information.
The Google DeepMind team has developed AlphaQubit, a recurrent, transformer-based neural network that learns to decode the surface code, the leading quantum error-correction code.
AlphaQubit outperforms other state-of-the-art decoders on real-world data from Google’s Sycamore quantum processor for distance-3 and distance-5 surface codes and on simulated data up to distance 11.
This 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.
The BuzzRobot guest, Thomas Edlich, Senior Research Engineer in the Science team at Google DeepMind, will present this work to our community.