Designing Quantum Error Correcting Codes to fit decoders via Reinforcement Learning

Abstract:We present a reinforcement learning (RL) approach to the co-design of stabilizer sets of Quantum Error Correcting Codes (QECCs) and decoders. We show how to produce a generative model that produces Bivariate Bicycle (BB) codes based on the choice of decoder. Specifically, we fix a decoder architecture and use Proximal Policy Optimisation (PPO) to train an agent over BB codes to maximise decoder performance under a depolarising channel noise model.

Quantum Physics (quant-ph); Information Theory (cs.IT)

arXiv:2608.15754 [quant-ph]

(or arXiv:2608.15754v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2608.15754 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.