Deep Neural Network Probabilistic Decoder for Stabilizer Codes

Abstract

Neural networks can efficiently encode the probability distribution of errors in an error correcting code. Moreover, these distributions can be conditioned on the syndromes of the corresponding errors. This paves a path forward for a decoder that employs a neural network to calculate the conditional distribution, then sample from the distribution - the sample will be the predicted error for the given syndrome. We present an implementation of such an algorithm that can be applied to any stabilizer code. Testing it on the toric code, it has higher threshold than a number of known decoders thanks to naturally finding the most probable error and accounting for correlations between errors.

References in corpus (3)

• Quantum Error Correction
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Decoding Small Surface Codes with Feedforward Neural Networks

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