ADaPT: Adaptive-window Decoding for Practical fault-Tolerance
For quantum computing researchers, this work addresses the practical bottleneck of decoding latency in scalable fault-tolerant computation by reducing unnecessary overhead.
ADaPT reduces decoding time overhead in fault-tolerant quantum error correction by adaptively adjusting window size based on decoder confidence, leveraging the sparsity of average-case errors. It achieves target error rates with low decoding time overhead across various codes and noise models.
Window decoding, first proposed to reduce decoding complexity for real-time decoding, is an essential component to realize scalable, universal-fault tolerant computation. Prior work has focused on improving throughput through parallelization and reducing reaction time via speculation on window boundaries. However, these methods use a fixed window size d, paying a fixed decoding time overhead for each window. In practice, we find this overhead of a fixed window size unnecessary in many cases due to the sparsity of average-case errors in QEC. Leveraging this insight, in this paper we propose an adaptive window decoding technique based on decoder confidence. This technique reduces the overhead in decoding time thus reducing reaction time without compromising on logical error rates. We benchmark adaptive window decoding across different codes and hardware inspired noise models. Our results show that this adaptive technique reaches the target error rate while maintaining a low decoding time overhead across different codes, and under different noise models.