Quantum Benchmarking

中文速览 本文提出了一种名为“子电路体积基准测试”（Subcircuit Volumetric Benchmarking, SVB）的创新方法，旨在解决评估当前量子计算机执行未来大规模、实用级量子算法能力的难题。由于现有硬件的规模和噪声水平有限，无法直接运行这些庞大的“目标”算法，因此难以衡量技术进展。SVB方法的核心思想是，从一个编译好的、任意大的目标算法电路中“剪切”出许多不同宽度（量子比特数）和深度（门层数）的小型子电路片段。随后，在实际的量子硬件上运行这些可管理的片段，并高效地测量它们的执行质量（具体为过程保真度）。通过分析这些片段的性能如何随其尺寸变化，该方法不仅能直观地展示设备的性能瓶颈，还能外推出整个目标电路的预期保真度，并最终计算出一个简洁的“能力系数”，用以量化当前系统距离成功执行目标算法还有多远。该方法具有可扩展性，能够为追踪量子实用性的进展提供一个稳定且有针对性的衡量标准。 English Research Briefing Research Briefing: Benchmarking quantum computers with any quantum algorithm 1. The Core Contribution This paper introduces Subcircuit Volumetric Benchmarking (SVB), a novel and scalable method for assessing a quantum computer’s performance on any target quantum algorithm, even those far too large to run on current hardware. The central thesis is that by systematically “snipping” small, executable subcircuits from a utility-scale target circuit and measuring their process fidelity, one can realistically predict the performance on the full circuit and track progress toward quantum utility. The primary conclusion, demonstrated on IBM Q systems, is that this method is not only practical but also reveals crucial performance limitations missed by simpler benchmarks. Specifically, it shows that optimistic fidelity predictions based on small-scale (e.g., 2-qubit) tests are misleading, with realistic performance on wider circuits being orders of magnitude worse due to the severe impact of crosstalk and other correlated errors. SVB distills this complex performance into a single, intuitive capability coefficient that quantifies how close a system is to successfully executing a given large-scale application. ...