Leapfrogging Sycamore: harnessing 1432 GPUs for 7× faster quantum random circuit sampling
Xian-He Zhao, Han-Sen Zhong, Feng Pan, Zihan Chen, Rongxin Fu, Z. B. Su, Xiaotong Xie, Chaoxing Zhao, Pan Zhang, Wanli Ouyang, Chao‐Yang Lu, Jian-Wei Pan, Ming-Cheng Chen · National Science Review · 2024
ABSTRACT Random quantum circuit sampling serves as a benchmark to demonstrate quantum computational advantage. Recent progress in classical algorithms, especially those based on tensor network methods, has significantly reduced the classical simulation time and challenged the claim of first-generation quantum advantage experiments. However, in terms of generating uncorrelated samples, time to solution and energy consumption, previous classical simulation experiments still underperform the Sycamore processor. Here we report an energy-efficient classical simulation algorithm, using 1432 GPUs to simulate quantum random circuit sampling that generates uncorrelated samples with a higher linear cross-entropy score and is 7$\times$ faster than the Sycamore 53-qubit experiment. We propose a post-processing algorithm to reduce the overall complexity, and integrate state-of-the-art high-performance general-purpose GPUs to achieve two orders of lower energy consumption compared to previous works. Our work provides the first unambiguous experimental evidence to refute Sycamore’s claim of quantum advantage, and redefines the boundary of quantum computational advantage using random circuit sampling.