Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation
Quantum error correction (QEC) is essential for scalable quantum computing. However, it requires classical decoders that are fast and accurate enough to keep pace with quantum hardware. While quantum low-density parity-check codes have recently emerged as a promising route to efficient fault tolerance, current decoding algorithms do not allow one to realize the full potential of these codes in practical settings. Here, we introduce a convolutional neural network decoder that exploits the geometric structure of QEC codes, and use it to probe a novel "waterfall" regime of error suppression, demonstrating that the logical error rates required for large-scale fault-tolerant algorithms are att...
| Authors | Andi Gu, J. Pablo Bonilla Ataides, Mikhail D. Lukin et al. |
|---|---|
| Primary category | quant-ph |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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