Abstract
In this work we demonstrate experimentally how generative model training can be used as a benchmark for small (fewer than five qubits) quantum devices. Performance is quantified using three data analytic metrics: the Kullback-Leibler divergence and two adaptations of the score. Using the bars and stripes data set, we train several different circuit constructions for generative modeling with superconducting qubits. By taking hardware connectivity constraints into consideration, we show that sparsely connected shallow circuits outperform denser counterparts on noisy hardware.
5 More- Received 5 December 2018
- Revised 4 May 2019
- Corrected 7 October 2020
DOI:https://doi.org/10.1103/PhysRevA.99.062323
©2019 American Physical Society
Physics Subject Headings (PhySH)
Corrections
7 October 2020
Correction: The previously published Figure 1 contained an error and has been replaced.