Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implementations. To decrease the decoding time, we reduce the decoding problem to a classification problem that a feedforward neural network can solve. We investigate quantum error correction and fault tolerance at small code distances using neural network-based decoders, demonstrating that the neural network can generalize to inputs that were not provided during training and that they can reach similar or better decoding performance compared to previous algorithms. We conclude by discussing the time required by a feedforward neural network decoder in hardware.

Original languageEnglish
Article number015004
Pages (from-to)1-12
Number of pages12
JournalQuantum Science and Technology
Issue number1
Publication statusPublished - 1 Jan 2018

    Research areas

  • artificial neural networks, fault tolerance, quantum error correction, surface codes

ID: 45481611