Neural Decoders for Universal Quantum Algorithms

J. Pablo Bonilla Ataides, Andi Gu, Susanne F. Yelin, Mikhail D. Lukin · 2025 · arXiv

| 编号 QE274 | 主题 译码算法 | 链接 arXiv:2509.11370 |

摘要

摘要保留原文（英文）

摘要取自 arXiv / 出版方元数据，未作翻译，以保证与原文一致。

Fault-tolerant quantum computing demands decoders that are fast, accurate, and adaptable to circuit structure and realistic noise. While machine learning (ML) decoders have demonstrated impressive performance for quantum memory, their use in algorithmic decoding - where logical gates create complex error correlations - remains limited. We introduce a modular attention-based neural decoder that learns gate-induced correlations and generalizes from training on random circuits to unseen multi-qubit algorithmic workloads. Our decoders achieve fast inference and logical error rates comparable to mo

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