Problem
Research questions and friction points this paper is trying to address.
Quantum Error Correcting Codes
Decoder Co-design
Reinforcement Learning
Bivariate Bicycle Codes
Designing Quantum Error Correcting Codes to fit decoders via Reinforcement Learning
🤖 AI Summary
This study addresses the challenge of co-designing quantum error-correcting codes and decoders by proposing a reinforcement learning-based joint generation framework. Leveraging the Proximal Policy Optimization algorithm, this method trains an agent to automatically generate bivariate bicycle code stabilizers tailored to specific decoders. Experimental results demonstrate that the proposed framework effectively achieves deep coupling between codes and decoders, significantly enhancing error correction performance under depolarizing noise channels. By overcoming the limitations of traditional separate design paradigms, this work provides a novel methodology and empirical evidence for constructing high-performance quantum error correction systems.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reinforcement Learning
Co-design
Quantum Error Correcting Codes
Proximal Policy Optimisation
Bivariate Bicycle codes
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