一句话：提出轻量级低延迟查找表解码器，用FPGA实现量子纠错实时解码，并以压缩查找表大幅降低内存。
量子设备错误率远高于量子应用需求，量子纠错需实时解码综合征，但软件解码器慢、硬件解码器精度低，现有研究多依赖离线解码。本文提出 LILLIPUT：将综合征转换为错误检测事件并索引查找表，实时获得错误信息；查找表由软件解码器离线为所有可能错误事件编程。它可容忍量子硬件上任一操作（含门和测量）的错误，容忍错误数随码尺寸增长。提出压缩查找表 CLUT，利用错误事件概率不均，仅存储最可能事件。评估显示其占用不到 7% FPGA 逻辑，延迟仅几纳秒，实现实时解码；CLUT 将内存从 148 MB 降至 1.38 MB，压缩 107 倍且不损失精度。
The error rates of quantum devices are orders of magnitude higher than what is needed to run most quantum applications. To close this gap, Quantum Error Correction (QEC) encodes logical qubits and distributes information using several physical qubits. By periodically executing a syndrome extraction circuit on the logical qubits, information about errors (called syndrome) is extracted while running programs. A decoder uses these syndromes to identify and correct errors in real time, which is necessary to prevent accumulation of errors. Unfortunately, software decoders are slow and hardware decoders are fast but less accurate. Thus, almost all QEC studies so far have relied on offline decoding. To enable real-time decoding in near-term QEC, we propose LILLIPUT– a Lightweight Low Latency Look-Up Table decoder. LILLIPUT consists of two parts– First, it translates syndromes into error detection events that index into a Look-Up Table (LUT) whose entry provides the error information in real-time. Second, it programs the LUTs with error assignments for all possible error events by running a software decoder offline. LILLIPUT tolerates an error on any operation in the quantum hardware, including gates and measurements, and the number of tolerated errors grows with the size of the code. LILLIPUT utilizes less than 7% logic on off-the-shelf FPGAs enabling practical adoption, as FPGAs are already used to design the control and readout circuits in existing systems. LILLIPUT incurs a latency of a few nanoseconds and enables real-time decoding. We also propose Compressed LUTs (CLUTs) to reduce the memory required by LILLIPUT. By exploiting the fact that not all error events are equally likely and only storing data for the most probable error events, CLUTs reduce the memory needed by up-to 107x (from 148 MB to 1.38 MB) without degrading the accuracy.
@inproceedings{0005LJ22,
title = {LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction},
author = {Poulami Das and Aditya Locharla and Cody Jones},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507707}
}