Union-find decoder for surface code achieves finite threshold under circuit-level stochastic errors with quasi-polylog parallel runtime bound.
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AI pre-decoders achieve O(1 μs) per round decoding runtimes on GPUs for surface codes while improving logical error rates over global decoding alone and enabling data-driven noise weight estimation.
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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Proof of a finite threshold for the union-find decoder
Union-find decoder for surface code achieves finite threshold under circuit-level stochastic errors with quasi-polylog parallel runtime bound.
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Fast and accurate AI-based pre-decoders for surface codes
AI pre-decoders achieve O(1 μs) per round decoding runtimes on GPUs for surface codes while improving logical error rates over global decoding alone and enabling data-driven noise weight estimation.
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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.