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Quantum Machine Learning
Superconducting Qubits
Generative model benchmarks for superconducting qubits
arXiv
Authors: Kathleen E. Hamilton, Eugene F. Dumitrescu, Raphael C. Pooser
Year
2018
Paper ID
23150
Status
Preprint
Abstract Read
~2 min
Abstract Words
79
Citations
N/A
Abstract
In this work we experimentally demonstrate how generative model training can be used as a benchmark for small (<5 qubits) quantum devices. Performance is quantified using three data analytic metrics: the Kullbeck-Leiber divergence, and two adaptations of the F1 score. Using the 2times2 Bars and Stripes dataset, we determine optimal circuit constructions for generative model training on superconducting qubits by including hardware connectivity constraints into circuit design. We show that on noisy hardware sparsely connected, shallow circuits out-perform denser counterparts.
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