ADaPT: Adaptive-window Decoding for Practical fault-Tolerance

TL;DR

This paper introduces ADaPT, an adaptive window decoding method that reduces decoding time overhead in fault-tolerant quantum computing by leveraging decoder confidence, without sacrificing error rates.

Contribution

It proposes a novel adaptive window decoding technique based on decoder confidence, improving efficiency over fixed window methods in quantum error correction.

Findings

Reduces decoding time overhead across various codes and noise models.

Maintains target error rates while lowering reaction time.

Effective in practical fault-tolerance scenarios.

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.