HyQuas

Abstract

Quantum computing has shown its strong potential in solving certain important problems. Due to the intrinsic limitations of current real quantum computers, quantum circuit simulation still plays an important role in both research and development of quantum computing. GPU-based quantum circuit simulation has been explored due to GPU's high computation capability. Despite previous efforts, existing quantum circuit simulation systems usually rely on a single method to improve poor data locality caused by complex quantum entanglement. However, we observe that existing simulation methods show significantly different performance for different circuit patterns. The optimal performance cannot be obtained only with any single method. To address these challenges, we propose HyQuas, a \textbf{Hy}brid partitioner based \textbf{Qua}ntum circuit \textbf{S}imulation system on GPU, which can automatically select the suitable simulation method for different parts of a given quantum circuit according to its pattern. Moreover, to make better support for HyQuas, we also propose two highly optimized methods, OShareMem and TransMM, as optional choices of HyQuas. We further propose a GPU-centric communication pipelining approach for effective distributed simulation. Experimental results show that HyQuas can achieve up to 10.71 x speedup on a single GPU and 227 x speedup on a GPU cluster over state-of-the-art quantum circuit simulation systems.