Leapfrogging Sycamore: Harnessing 1432 GPUs for 7$\times$ Faster Quantum Random Circuit Sampling

TL;DR

This paper demonstrates a classical simulation of quantum random circuit sampling using 1432 GPUs, achieving 7 times faster results and lower energy consumption than Google's Sycamore quantum processor, challenging claims of quantum advantage.

Contribution

The authors develop a new energy-efficient classical simulation algorithm that surpasses Sycamore's performance in speed and energy use, redefining quantum advantage boundaries.

Findings

Classical simulation achieved 7x speed of Sycamore

Simulation used 1432 GPUs with lower energy consumption

Provides evidence refuting Sycamore's quantum advantage claim

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 the 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 \textit{Sycamore} processor. Here we report an energy-efficient classical simulation algorithm, using 1432 GPUs to simulate quantum random circuit sampling which generates uncorrelated samples with higher linear cross entropy score and is 7 times faster than \textit{Sycamore} 53 qubits experiment. We propose a post-processing algorithm to reduce the overall complexity, and integrated state-of-the-art high-performance general-purpose GPU to achieve two orders of lower energy consumption compared to previous works. Our work provides the first unambiguous experimental evidence to refute \textit{Sycamore}'s claim of quantum advantage, and redefines the boundary of quantum computational advantage using random circuit sampling.