A NEAT Quantum Error Decoder

TL;DR

This paper explores using the NEAT evolutionary algorithm to optimize quantum error decoders, achieving comparable performance to existing machine learning methods with significantly fewer parameters.

Contribution

It introduces a NEAT-based approach for quantum error decoding that reduces model complexity while maintaining accuracy.

Findings

NEAT-optimized decoders perform similarly to existing ML decoders

Decoders use 3-4 orders of magnitude fewer parameters

Effective on toric code with bitflip and depolarizing noise

Abstract

We investigate the use of the evolutionary NEAT algorithm for the optimization of a policy network that performs quantum error decoding on the toric code, with bitflip and depolarizing noise, one qubit at a time. We find that these NEAT-optimized network decoders have similar performance to previously reported machine-learning based decoders, but use roughly three to four orders of magnitude fewer parameters to do so.