一句话：提出多稀释制冷机分布式量子计算机的软硬件协同容错纠错机制，实现百万量子比特级容错计算。
百万量子比特级量子计算机需集成多个稀释制冷机（DR）中的量子处理器，但跨 DR 纠缠速度慢、错误率高且量子比特规模增大，使传统纠错因门操作数多和跨 DR 解码延迟大而失效。本文提出面向多 DR 分布式量子计算机的容错错误处理机制，通过软硬件协同设计实现：低开销多 DR 错误症状测量（ESM）序列以减少门操作数和错误率，以及可扩展多 DR 错误解码单元（EDU）架构以提升解码速度与精度。相比基线，ESM 延迟、ESM 错误、EDU 延迟和 EDU 精度分别提升 3.7 倍、2.4 倍、685 倍和 6.1×10^10 倍；在电压缩放 CMOS 与成熟 ERSFQ 技术假设下，构建了容错百万量子比特级量子计算机。
A million qubit-scale quantum computer is essential to realize the quantum supremacy. Modern large-scale quantum computers integrate multiple quantum computers located in dilution refrigerators (DR) to overcome each DR's unscaling cooling budget. However, a large-scale multi-DR quantum computer introduces its unique challenges (i.e., slow and erroneous inter-DR entanglement, increased qubit scale), and they make the baseline error handling mechanism ineffective by increasing the number of gate operations and the inter-DR communication latency to decode and correct errors. Without resolving these challenges, it is impossible to realize a fault-tolerant large-scale multi-DR quantum computer. In this paper, we propose a million qubit-scale distributed quantum computer which uses a novel error handling mechanism enabling fault-tolerant multi-DR quantum computing. First, we apply a low-overhead multi-DR error syndrome measurement (ESM) sequence to reduce both the number of gate operations and the error rate. Second, we apply a scalable multi-DR error decoding unit (EDU) architecture to maximize both the decoding speed and accuracy. Our multi-DR error handling SW-HW co-design improves the ESM latency, ESM errors, EDU latency, and EDU accuracy by 3.7 times, 2.4 times, 685 times, and 6.1 · 1010 times, respectively. With our scheme applied to assumed voltage-scaled CMOS and mature ERSFQ technologies, we successfully build a fault-tolerant million qubit-scale quantum computer.
@inproceedings{KimMCJBCHK24,
title = {A Fault-Tolerant Million Qubit-Scale Distributed Quantum Computer},
author = {Junpyo Kim and Dongmoon Min and Jungmin Cho and Hyeonseong Jeong and Ilkwon Byun and Junhyuk Choi and Juwon Hong and Jangwoo Kim},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620665.3640388}
}