异质HPC系统上量子电路的高性能状态向量模拟

Bo Zhang, B. Fang, Qiang Guan, A. Li, Dingwen Tao
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引用次数: 0

摘要

随着量子计算领域的发展,量子电路仿真的应用越来越广泛。它有助于研究人员评估量子算法,减轻有限量子计算资源的负担。然而,大多数最先进的量子模拟器使用CPU或GPU来存储和计算状态向量,这导致资源浪费。此外,模拟器支持的最大量子位数受内存限制,因为内存利用率随着量子位数的增加呈指数增长。在本研究中,我们利用异构计算来利用CPU和GPU来存储和更新状态向量。我们还集成了有损数据压缩以减少内存需求。具体来说,我们开发了一个具有动态调度程序的异构框架,以充分利用计算资源。我们对分块状态向量进行有损压缩,使其最大量子位数高于常规模拟器,压缩也有利于CPU和GPU之间的数据移动。
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HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems
Quantum circuit simulations are applied in more and more circumstances as the quantum computing community becomes broader. It helps researchers to evaluate the quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilizes either CPU or GPU to store and calculate the state vector, which results in resources stravation. Morever, the mamximum number of qubits supported by simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benifits the data movement between CPU and GPU.
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Advancing Comprehension of Quantum Application Outputs: A Visualization Technique Efficient QAOA Optimization using Directed Restarts and Graph Lookup Robust and Efficient Quantum Communication Quantum Reinforcement Learning for Quantum Architecture Search HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems
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