Transformer neural networks estimate coherent information thresholds for the surface code under code capacity, phenomenological, and circuit-level noise, outperforming minimum weight perfect matching decoding and enabling optimal soft post-selection.
Low-distance surface codes under realistic quantum noise
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LDGM codes enable syndrome measurement for QLDPC codes with controlled constant-weight stabilizers, yielding lower logical error rates than repeated extraction on a distance-5 surface code.
Three architectural types for fault-tolerant distributed quantum computing exhibit distinct scaling of Bell-pair consumption and generation attempts with code distance in planar surface and toric codes.
citing papers explorer
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Machine Learning Optimal Quantum Error Correction Thresholds
Transformer neural networks estimate coherent information thresholds for the surface code under code capacity, phenomenological, and circuit-level noise, outperforming minimum weight perfect matching decoding and enabling optimal soft post-selection.
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Fault-Tolerant QLDPC Syndrome Measurement via LDGM Encoding
LDGM codes enable syndrome measurement for QLDPC codes with controlled constant-weight stabilizers, yielding lower logical error rates than repeated extraction on a distance-5 surface code.
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Architectural Approaches to Fault-Tolerant Distributed Quantum Computing and Their Entanglement Overheads
Three architectural types for fault-tolerant distributed quantum computing exhibit distinct scaling of Bell-pair consumption and generation attempts with code distance in planar surface and toric codes.