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Deep Neural Network Probabilistic Decoder for Stabilizer Codes
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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.
Forward citations
Cited by 2 Pith papers
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Geometric Algebra Quantum Gate Decomposition
Reformulates Pauli and Clifford groups in geometric algebra with a greedy rotor decomposition algorithm for Clifford operators and geometric view of Clifford+T universality.
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A value-iteration network with global phase invariance, unitary perturbation augmentation, and NeRF-style encoding synthesizes 2- and 3-qubit Clifford+T circuits with fewer T gates than the Quantum Shannon Decompositi...
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