Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version)

• URL: http://arxiv.org/abs/2408.09055v2
• Date: Mon, 4 Nov 2024 21:59:24 GMT
• Title: Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version)
• Authors: Mingkuan Xu, Shiyi Cao, Xupeng Miao, Umut A. Acar, Zhihao Jia,
• Abstract summary: This paper presents techniques for theoretically and practically efficient and scalable quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates subcircuits on multi-node GPU. To minimize communication costs, we formulate an Linear Program that rewards simulation of "nearby" gates on "nearby" To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU.
• Score: 9.483321080040131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents techniques for theoretically and practically efficient and scalable Schr\"odinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2$\times$ on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.

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