  Syndrome decoding is an integral but computationally demanding step in the
implementation of quantum error correction for fault-tolerant quantum
computing. Here, we report the development and benchmarking of Artificial
Neural Network (ANN) decoding on IBM Quantum Processors. We demonstrate that
ANNs can efficiently decode syndrome measurement data from heavy-hexagonal code
architecture and apply appropriate corrections to facilitate error protection.
The current physical error rates of IBM devices are above the code's threshold
and restrict the scope of our ANN decoder for logical error rate suppression.
However, our work confirms the applicability of ANN decoding methods of
syndrome data retrieved from experimental devices and establishes machine
learning as a promising pathway for quantum error correction when quantum
devices with below threshold error rates become available in the near future.

Quantum Physics