Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks -- such as algorithmic or randomized benchmarks -- typically do not predict a processor's performance on circuits outside the benchmark's necessarily very limited set of test circuits. In this paper, we introduce a general framework for building predictive models from benchmarking data using capability models. Capability models can be fit to many kinds of benchmarking data and used for a variety of predictive tasks. We demonstrate this flexibility with two case studies. In the first case study, we predict circuit (i) process fidelities and (ii) success probabilities by fitting error rates models to two kinds of volumetric benchmarking data. Error rates models are simple, yet versatile capability models which assign effective error rates to individual gates, or more general circuit components. In the second case study, we construct a capability model for predicting circuit success probabilities by applying transfer learning to ResNet50, a neural network trained for image classification. Our case studies use data from cloud-accessible quantum computers and simulations of noisy quantum computers.

翻译：量子计算机的整体基准测试对于测试和总结量子硬件的性能至关重要。然而，整体基准测试（例如算法基准测试或随机基准测试）通常无法预测处理器在基准测试所限定的极少量测试电路之外的电路上的性能。本文提出了一种通用框架，利用能力模型从基准测试数据构建预测模型。能力模型可适配多种基准测试数据，并用于多种预测任务。我们通过两个案例研究展示了这种灵活性。第一个案例中，我们通过将错误率模型拟合至两种体积基准测试数据，预测电路的（i）过程保真度和（ii）成功概率。错误率模型是简单但通用的能力模型，可为单个门或更广泛的电路组件分配有效错误率。第二个案例中，我们通过将迁移学习应用于为图像分类训练的神经网络ResNet50，构建了用于预测电路成功概率的能力模型。我们的案例研究使用了来自云端可访问量子计算机及噪声量子计算机模拟的数据。