ADaPT: Adaptive-window Decoding for Practical fault-Tolerance

2026-05-01

AI Summary

This work addresses the inherent trade-off in fixed-window decoding for quantum error correction, where a static decoding window introduces latency that impedes both low reaction time and target logical error rates. To overcome this limitation, the authors propose an adaptive window decoding scheme that dynamically adjusts the decoding window size based on the decoder’s confidence level—a novel application of confidence estimation in this context. By integrating this approach with various quantum error-correcting codes under hardware-inspired noise models, the method maintains the desired logical error rate while significantly reducing average decoding latency. Experimental results demonstrate consistent reductions in time overhead across diverse code families and noise configurations, thereby surpassing the efficiency constraints of conventional fixed-window strategies.

Abstract

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.

Problem

• window decoding
• fault tolerance
• quantum error correction
• decoding overhead
• adaptive decoding

Innovation

• adaptive-window decoding
• fault-tolerant quantum computing
• decoder confidence
• quantum error correction
• real-time decoding