Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection. The QUantum computing Application benchmaRK (QUARK) framework simplifies and standardizes benchmarking studies for quantum computing applications. Here, we propose several extensions of QUARK to include the ability to evaluate the training and deployment of quantum generative models. We describe the updated software architecture and illustrate its flexibility through several example applications: (1) We trained different quantum generative models using several circuit ansatzes, data sets, and data transformations. (2) We evaluated our models on GPU and real quantum hardware. (3) We assessed the generalization capabilities of our generative models using a broad set of metrics that capture, e.g., the novelty and validity of the generated data.

翻译：量子机器学习（QML）算法的基准测试因QML系统在模型拟设、数据集、训练技术和超参数选择等方面的复杂性与多样性而颇具挑战性。量子计算应用基准测试框架QUARK简化和标准化了量子计算应用的基准测试研究。本文提出了QUARK的若干扩展，使其具备评估量子生成模型训练与部署的能力。我们描述了更新后的软件架构，并通过多个示例应用展示了其灵活性：（1）利用多种电路拟设、数据集和数据变换训练了不同的量子生成模型；（2）在GPU和真实量子硬件上评估了模型性能；（3）通过涵盖生成数据的新颖性与有效性等维度的广泛度量指标，评估了生成模型的泛化能力。