The END: An Equivariant Neural Decoder for Quantum Error Correction
This work addresses the critical need for scalable decoders in quantum computing, offering a data-efficient solution that could enhance error correction in quantum systems.
The authors tackled the problem of efficient quantum error correction by introducing an equivariant neural decoder that exploits symmetries in the toric code, achieving state-of-the-art accuracy compared to previous neural decoders.
Quantum error correction is a critical component for scaling up quantum computing. Given a quantum code, an optimal decoder maps the measured code violations to the most likely error that occurred, but its cost scales exponentially with the system size. Neural network decoders are an appealing solution since they can learn from data an efficient approximation to such a mapping and can automatically adapt to the noise distribution. In this work, we introduce a data efficient neural decoder that exploits the symmetries of the problem. We characterize the symmetries of the optimal decoder for the toric code and propose a novel equivariant architecture that achieves state of the art accuracy compared to previous neural decoders.