Quantum Brain
← Back to papers

Alibaba Cloud Quantum Development Kit: Large-Scale Classical Simulation of Quantum Circuits

Fang Zhang, Cupjin Huang, M. Newman, Junjie Cai, Huanjun Yu, Zhengxiong Tian, Bo Yuan, Haihong Xu, Junyin Wu, Xun Gao, Jianxin Chen, M. Szegedy, Yaoyun Shi·July 25, 2019
Physics

AI Breakdown

Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.

Abstract

We report, in a sequence of notes, our work on the Alibaba Cloud Quantum Development Kit (AC-QDK). AC-QDK provides a set of tools for aiding the development of both quantum computing algorithms and quantum processors, and is powered by a large-scale classical simulator deployed on Alibaba Cloud. In this note, we report the computational experiments demonstrating the classical simulation capability of AC-QDK. We use as a benchmark the random quantum circuits designed for Google's Bristlecone QPU {\cite{GRCS}}. We simulate Bristlecone-70 circuits with depth $1 + 32 + 1$ in $0.43$ second per amplitude, using $1449$ Alibaba Cloud Elastic Computing Service (ECS) instances, each with $88$ Intel Xeon(Skylake) Platinum 8163 vCPU cores @ 2.5 GHz and $160$ gigabytes of memory. By comparison, the previously best reported results for the same tasks are $104$ and $135$ seconds, using NASA's HPC Pleiades and Electra systems, respectively ({arXiv:1811.09599}). Furthermore, we report simulations of Bristlecone-70 with depth $1+36+1$ and depth $1+40+1$ in $5.6$ and $580.7$ seconds per amplitude, respectively. To the best of our knowledge, these are the first successful simulations of instances at these depths.

Related Research

Quantum Intelligence

Ask about quantum research, companies, or market developments.