# Decoding Small Surface Codes with Feedforward Neural Networks

## TL;DR

This paper explores using feedforward neural networks to decode small surface codes efficiently, achieving comparable or better performance than traditional algorithms while potentially reducing decoding time for quantum error correction.

## Contribution

It introduces a neural network-based decoding approach for small surface codes, demonstrating generalization and performance improvements over existing algorithms.

## Key findings

- Neural network decoders can generalize to unseen inputs.
- Neural decoders achieve similar or better accuracy than traditional algorithms.
- Potential for faster decoding in hardware implementations.

## Abstract

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.