论文标题
全面量子计算堆栈的应用动机动机，整体基准测试
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack
论文作者
论文摘要
量子计算系统需要根据预期的实际任务进行基准测试。在这里，我们提出了3个用于基准测试的“应用程序动机”电路类：深层（与变量量子本质量算法中的状态制备相关），浅层（灵感来自IQP型电路，这些电路可能对近端量子量子的机器学习有用）和Square（灵感来自量子量量基准）。我们使用几个功绩数字量化了从这些类中运行电路中的量子计算系统的性能，所有这些都需要指数的经典计算资源和系统中多项式的经典样本（Bitsring）。我们研究性能如何随所使用的编译策略以及运行电路的设备而变化。使用IBM量子提供的系统，我们检查了它们的性能，表明噪声吸引的汇编策略可能是有益的，并且该设备的连接性和噪声水平在根据我们的基准测试中在系统的性能中起着至关重要的作用。
Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. Here, we propose 3 "application-motivated" circuit classes for benchmarking: deep (relevant for state preparation in the variational quantum eigensolver algorithm), shallow (inspired by IQP-type circuits that might be useful for near-term quantum machine learning), and square (inspired by the quantum volume benchmark). We quantify the performance of a quantum computing system in running circuits from these classes using several figures of merit, all of which require exponential classical computing resources and a polynomial number of classical samples (bitstrings) from the system. We study how performance varies with the compilation strategy used and the device on which the circuit is run. Using systems made available by IBM Quantum, we examine their performance, showing that noise-aware compilation strategies may be beneficial, and that device connectivity and noise levels play a crucial role in the performance of the system according to our benchmarks.
