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.
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 2 Hugo Théveniaut,
- 1 Everard van Nieuwenburg
- Agence Nationale de la Recherche [ANR]
- Horizon 2020 (through Organization: European Commission [EC])
- Partnership for Advanced Computing in Europe AISBL (PRACE) (through Organization: Partnership for Advanced Computing in Europe [PRACE])