Efficient depth selection for the implementation of noisy quantum approximate optimization algorithm

DOI10.1016/j.jfranklin.2022.10.027OpenAlexW4307956198MaRDI QIDQ2109217

Shibei Xue, Yu Pan, Yifan Tong, Guo-Feng Zhang

Publication date: 20 December 2022

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.04263

Mathematics Subject Classification ID

Computer science (68-XX)

Related Items (1)

Efficient quantum feature extraction for CNN-based learning

Cites Work

• Unnamed Item
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Empirical performance bounds for quantum approximate optimization
• Lower bounds on circuit depth of the quantum approximate optimization algorithm
• Statistical Learning Theory: Models, Concepts, and Results
• Model Selection and Multimodel Inference
• An Introduction to Statistical Learning

This page was built for publication: Efficient depth selection for the implementation of noisy quantum approximate optimization algorithm