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 F1 score. Using the 2×2 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.
| Original language | English |
|---|---|
| Article number | 062323 |
| Journal | Physical Review A |
| Volume | 99 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 18 2019 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This work was supported as part of the ASCR Testbed Pathfinder Program at Oak Ridge National Laboratory under FWP Project No. ERKJ332. This research used quantum computing system resources of the Oak Ridge Leadership Computing Facility. Oak Ridge National Laboratory manages access to the IBM Q System as part of the IBM Q Network.
| Funders | Funder number |
|---|---|
| FWP | ERKJ332 |
| U.S. Department of Energy | |
| Advanced Scientific Computing Research | |
| Oak Ridge National Laboratory |