Isaac Turtletaub

College of Engineering

Works (1)

Updated: April 11th, 2023 10:13

2020 article

Application of Quantum Machine Learning to VLSI Placement

PROCEEDINGS OF THE 2020 ACM/IEEE 2ND WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD '20), pp. 61–66.

By: I. Turtletaub n, G. Li n, M. Ibrahim n & P. Franzon n 

author keywords: Quantum Machine Learning; Balanced Min-Cut; Variational Quantum Eigensolver; Recursive Partitioning Placement
TL;DR: The Variational Quantum Eigensolver (VQE) is used to formulate a recursive Balanced Min-Cut (BMC) algorithm, and it is suggested that quantum machine learning techniques can lower error rates and allow for faster convergence to an optimal solution. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: August 16, 2021

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.