General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes
Amarsanaa Davaasuren, Yasunari Suzuki, Keisuke Fujii, Masato Koashi
Abstract
This work introduces a framework of machine-learning-based decoders for quantum error correction. Specifically, the authors show necessary and sufficient conditions for constructing high-performance decoders.
Topics & Concepts
Computer scienceDecoding methodsNoise (video)QuantumConvolutional neural networkAlgorithmArtificial neural networkCode (set theory)ScalabilitySet (abstract data type)Error detection and correctionTopology (electrical circuits)Computer engineeringTheoretical computer scienceArtificial intelligenceMathematicsDatabaseQuantum mechanicsCombinatoricsPhysicsImage (mathematics)Programming languageQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum-Dot Cellular Automata