超越Sycamore:利用1432个GPU进行7倍更快的量子随机电路采样
Xian-He Zhao1,2,3,4, Han-Sen Zhong4, Feng Pan2
1Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, University of Science and Technology of China, Hefei 230026, China.
National science review
|March 6, 2025
概括
经典模拟现在在随机电路采样中优于量子计算机,挑战了以前的量子优势要求. 这种新的节能方法比Sycamore处理器更快,耗电更少.
科学领域:
- 量子计算是一种量子计算.
- 计算复杂性 计算复杂性
- 高性能计算的高性能计算.
背景情况:
- 随机量子电路采样是量子计算优势的基准.
- 经典算法,特别是张量网络方法,已经得到了改进,挑战了早期的量子优势主张.
- 以前的经典模拟在样本质量,速度和能源效率方面落后于像Sycamore这样的量子处理器.
研究的目的:
- 开发一种高能效的经典模拟算法,用于随机量子电路采样.
- 在早期量子实验中挑战和重新定义量子优势的说法.
- 为了提供明确的证据反驳西卡莫尔处理器的量子优势主张.
主要方法:
- 开发了一种使用1432个图形处理单元 (GPU) 的节能经典模拟算法.
- 使用后处理算法来降低整体计算复杂度.
- 集成的最先进的高性能GPU,可显著降低能源消耗.
主要成果:
- 与西卡摩尔实验相比,生成了具有更高线性交叉分数的无关联样本.
- 实现了比Sycamore 53量子比特处理器快7倍的模拟速度.
- 与以前的经典模拟工作相比,能源消耗减少了两倍.
结论:
- 开发的经典模拟方法提供了第一个明确的证据,驳斥了Sycamore处理器的量子优势主张.
- 这项工作重新定义了量子计算优势在随机电路采样背景下的边界.
- 突出了先进的经典算法和硬件在量子计算领域的关键作用.
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