Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
This work provides a standardized tool for researchers in quantum computing to benchmark generative models, but it is incremental as it builds upon an existing framework.
The authors tackled the challenge of benchmarking quantum machine learning algorithms by extending the QUARK framework to evaluate quantum generative models, demonstrating its flexibility through training on various circuit ansatzes and datasets and evaluating on GPU and quantum hardware with multiple metrics.
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.