Robust quantum gates using smooth pulses and physics-informed neural networks
| dc.contributor.author | Güngördü, Utkan | |
| dc.contributor.author | Kestner, Jason | |
| dc.date.accessioned | 2020-12-09T18:34:08Z | |
| dc.date.available | 2020-12-09T18:34:08Z | |
| dc.date.issued | 2020-11-04 | |
| dc.description.abstract | The presence of decoherence in quantum computers necessitates the suppression of noise. Dynamically corrected gates via specially designed control pulses offer a path forward, but hardware-specific experimental constraints can cause complications. Here, we present a widely applicable method for obtaining smooth pulses which is not based on a sampling approach and does not need any assumptions with regards to the underlying statistics of the experimental noise. We demonstrate the capability of our approach by finding smooth shapes which suppress the effects of noise within the logical subspace as well as leakage out of that subspace. | en |
| dc.description.sponsorship | This research was sponsored by the Army Research Office (ARO), and was accomplished under Grant Number W911NF-17-1-0287 | en |
| dc.description.uri | https://arxiv.org/abs/2011.02512 | en |
| dc.format.extent | 6 pages | en |
| dc.genre | journal articles preprints | en |
| dc.identifier | doi:10.13016/m2tjnf-wokq | |
| dc.identifier.citation | Utkan Güngördü and J. P. Kestner, Robust quantum gates using smooth pulses and physics-informed neural networks, https://arxiv.org/abs/2011.02512 | en |
| dc.identifier.uri | http://hdl.handle.net/11603/20215 | |
| dc.language.iso | en | en |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Physics Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.title | Robust quantum gates using smooth pulses and physics-informed neural networks | en |
| dc.type | Text | en |