Neural decoders for surface-code QEC achieve practical microsecond FPGA latency when trained on large datasets with appropriate inductive biases and INT4 quantization, rather than relying on architectural complexity.
arXiv preprint arXiv:2511.01062 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.
citing papers explorer
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Rethink the Role of Neural Decoders in Quantum Error Correction
Neural decoders for surface-code QEC achieve practical microsecond FPGA latency when trained on large datasets with appropriate inductive biases and INT4 quantization, rather than relying on architectural complexity.
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MCMit: Mid-Circuit Measurement Error Mitigation
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.