Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-08
Main Authors: Kiwit, Florian J, Marso, Marwa, Ross, Philipp, Riofrío, Carlos A, Klepsch, Johannes, Luckow, Andre
Format: Article
Language:English
Subjects:
Algorithms
Benchmarks
Circuits
Computer architecture
Datasets
Machine learning
Quantum computing
Quarks
Training evaluation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2308.04082