用电路特征评估量子计算机性能，更全面反映真实计算能力。
Featuremetric benchmarking: Quantum computer benchmarks based on circuit features
- 根据电路深度、宽度等特征分析量子机表现，超越传统单一指标
- 在27量子比特的IBM Q与IonQ系统上验证，提升性能建模精度
- 适合关注量子硬件评估的研究者与开发者参考
衡量多量子比特量子计算机性能的简洁基准对实现有用量子计算至关重要。本文提出一种基于电路特征（如电路深度、宽度、双量子门密度、问题输入规模或算法深度）的性能变化来评估量子计算机表现的基准框架。该特征度量基准框架扩展了广泛使用的体积基准方法，能够建立更丰富、更真实的量子计算机性能模型。我们在最多27个量子比特的IBM Q和IonQ系统上展示了该基准的实现，并通过高斯过程回归从数据中生成性能概览。这些数据分析方法在体积基准的特殊情况下也具价值，可仅用少量电路数据生成直观的二维能力区域。
原文摘要 · Abstract (English)
Benchmarks that concisely summarize the performance of many-qubit quantum computers are essential for measuring progress towards the goal of useful quantum computation. In this work, we present a benchmarking framework that is based on quantifying how a quantum computer's performance on quantum circuits varies as a function of features of those circuits, such as circuit depth, width, two-qubit gate density, problem input size, or algorithmic depth. Our featuremetric benchmarking framework generalizes volumetric benchmarking -- a widely-used methodology that quantifies performance versus circuit width and depth -- and we show that it enables richer and more faithful models of quantum computer performance. We demonstrate featuremetric benchmarking with example benchmarks run on IBM Q and IonQ systems of up to 27 qubits, and we show how to produce performance summaries from the data using Gaussian process regression. Our data analysis methods are also of interest in the special case of volumetric benchmarking, as they enable the creation of intuitive two-dimensional capability regions using data from few circuits.
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