Quantum Error Correction (QEC) is essential for building robust, fault-tolerant quantum computers; however, the decoding process often presents a significant computational bottleneck. Tesseract is a novel Most-Likely-Error (MLE) decoder for QEC that employs the A* search algorithm to explore an exponentially large graph of error hypotheses, achieving high decoding speed and accuracy. This paper presents a systematic approach to optimizing the Tesseract decoder through low-level performance enhancements. Based on extensive profiling, we implemented four targeted optimization strategies, including the replacement of inefficient data structures, reorganization of memory layouts to improve cache hit rates, and the use of hardware-accelerated bit-wise operations. We achieved significant decoding speedups across a wide range of code families and configurations, including Color Codes, Bivariate-Bicycle Codes, Surface Codes, and Transversal CNOT Protocols. Our results demonstrate consistent speedups of approximately 2x for most code families, often exceeding 2.5x. Notably, we achieved a peak performance gain of over 5x for the most computationally demanding configurations of Bivariate-Bicycle Codes. These improvements make the Tesseract decoder more efficient and scalable, serving as a practical case study that highlights the importance of high-performance software engineering in QEC and providing a strong foundation for future research.

翻译：量子纠错（QEC）对于构建稳健、容错的量子计算机至关重要；然而，解码过程常常构成显著的计算瓶颈。Tesseract是一种用于QEC的新型最可能错误（MLE）解码器，它采用A*搜索算法探索指数级大的错误假设图，实现了高解码速度与精度。本文提出了一种通过底层性能增强来优化Tesseract解码器的系统方法。基于广泛的性能剖析，我们实施了四项针对性优化策略，包括替换低效数据结构、重组内存布局以提高缓存命中率，以及使用硬件加速的位操作。我们在包括颜色码、双变量自行车码、表面码和横向CNOT协议在内的多种码族与配置中实现了显著的解码加速。我们的结果表明，对于大多数码族实现了约2倍的稳定加速，且常超过2.5倍。值得注意的是，在双变量自行车码计算需求最高的配置中，我们实现了超过5倍的峰值性能提升。这些改进使Tesseract解码器更高效且可扩展，作为一个实用案例研究，突显了高性能软件工程在QEC中的重要性，并为未来研究提供了坚实基础。