Improving Quantum Circuit Synthesis with Machine Learning-Reference-Cited by-同舟云学术

Improving Quantum Circuit Synthesis with Machine Learning

Published:2023-09-17 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Quantum Computing and Engineering (QCE)
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Author:

Weiden Mathias¹,Younis Ed²,Kalloor Justin¹,Kubiatowicz John¹,Iancu Costin²

Affiliation:

1. University of California,Department of Electrical Engineering and Computer Science,Berkeley

2. Computational Research Division, Lawrence Berkeley National Laboratory

Funder

DOE

ASCR

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10313590/10313591/10313908.pdf?arnumber=10313908

Reference35 articles.

1. Reducing the number of non-clifford gates in quantum circuits;kissinger;ArXiv Quantum Physics eprints,2019

2. QFAST: Quantum synthesis using a hierarchical continuous circuit space;younis;2021 IEEE International Conference on Quantum Computing and Engineering (QCE),2020

3. Principal component analysis: a review and recent developments

4. Berkeley quantum synthesis toolkit (bqskit) v l;younis;Lawrence Berkeley National Laboratory (LBNL) Berkeley CA (United States) Tech Rep,2021

5. Implementing grover's al-gorithm on the ibm quantum computers;mandviwalla;2018 IEEE International Conference on Big Data (Big Data),0

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