Rethink the Role of Neural Decoders in Quantum Error Correction

• URL: http://arxiv.org/abs/2605.12046v1
• Date: Tue, 12 May 2026 12:26:43 GMT
• Title: Rethink the Role of Neural Decoders in Quantum Error Correction
• Abstract summary: Quantum error correction (QEC) is essential for enabling quantum advantages.<n>We revisit neural decoders for surface-code decoding under explicit accuracy-latency constraints.<n>We develop an end-to-end compression pipeline to evaluate their deployability and performance on FPGA hardware.
• Score: 12.434295501714582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum error correction (QEC) is essential for enabling quantum advantages, with decoding as a central algorithmic primitive. Owing to its importance and intrinsic difficulty, substantial effort has been made to QEC decoder design, among which neural decoders have recently emerged as a promising data-driven paradigm. Despite this progress, practical deployment remains hindered by a fundamental accuracy-latency tradeoff, often on the microsecond timescale. To address this challenge, here we revisit neural decoders for surface-code decoding under explicit accuracy-latency constraints, considering code distances up to d=9 (161 physical qubits). We unify and redesign representative neural decoders into five architectural paradigms and develop an end-to-end compression pipeline to evaluate their deployability and performance on FPGA hardware. Through systematic experiments, we reveal several previously underexplored insights: (i) near-term decoding performance is driven more by data scale than architectural complexity; (ii) appropriate inductive bias is essential for achieving high decoding accuracy; and (iii) INT4 quantization is a prerequisite for meeting microsecond-scale latency requirements on FPGAs. Together, these findings provide concrete guidance toward scalable and real-time neural QEC decoding.

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