Edge-coloring eliminates automorphisms in low-weight stabilizer subgraphs of generalized bicycle codes, enabling improved anisotropic min-sum decoding.
How to choose a decoder for a fault-tolerant quantum computer? the speed vs accuracy trade-off
4 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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A 3D convolutional neural network decoder for surface codes with circuit noise generalizes to distance-97 codes with thresholds up to 0.7% depolarizing noise and improved latency over MWPM above distance 33.
A two-level decoder scheduling framework reduces classical processing requirements for quantum error correction by 10-40% on fault-tolerant benchmarks by managing bursty workloads as shared resources.
citing papers explorer
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Edge-Based Anisotropic Decoding for Generalized Bicycle Codes
Edge-coloring eliminates automorphisms in low-weight stabilizer subgraphs of generalized bicycle codes, enabling improved anisotropic min-sum decoding.
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Fully convolutional 3D neural network decoders for surface codes with syndrome circuit noise
A 3D convolutional neural network decoder for surface codes with circuit noise generalizes to distance-97 codes with thresholds up to 0.7% depolarizing noise and improved latency over MWPM above distance 33.
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Managing Classical Processing Requirements for Quantum Error Correction
A two-level decoder scheduling framework reduces classical processing requirements for quantum error correction by 10-40% on fault-tolerant benchmarks by managing bursty workloads as shared resources.
- Mitigating Classical Resource Costs in Quantum Error Correction via Generalized qLDPC Predecoding