Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Unsupervised Machine Learning Control of Quantum Gates in Gate-Model Quantum Computers

Not Accessible

Your library or personal account may give you access


The precise and stable working of quantum gates in quantum computers is essential for any quantum computations. We define a machine learning-based framework for the unsupervised control of entangled quantum gates in gate-model quantum computers.

© 2018 The Author(s)

PDF Article
More Like This
Measurement Optimization for Gate-Model Quantum Computers

Laszlo Gyongyosi and Sandor Imre
JTu3A.50 Frontiers in Optics (FiO) 2019

Finding Broken Gates in Quantum Circuits–Exploiting Hybrid Machine Learning

Margarite L. LaBorde, Allee C. Rogers, and Jonathan P. Dowling
FTu8D.4 Frontiers in Optics (FiO) 2020

Photonic Crystals Band Diagrams Computation by Using Extreme Learning Machine

Adriano da Silva Ferreira, Gilliard Nardel Malheiros-Silveira, and Hugo Enrique Hernandez-Figueroa
JW4A.94 Frontiers in Optics (FiO) 2018


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access Optica Member Subscription

Select as filters

Select Topics Cancel
© Copyright 2022 | Optica Publishing Group. All Rights Reserved