Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/23803
Title: 
Authors: 
Keywords: 
Lie algebra
Group theory
Machine learning
Issue Date: 
2022
Citation: 
Du, G., Zhou, C., & Kwek, L.-C. (2022). Compression and reduction of N*1 states by unitary matrices. Quantum Information Processing, 21(2), Article 80. https://doi.org/10.1007/s11128-022-03409-9
Journal: 
Quantum Information Processing
Abstract: 
In recent experiments, the compression from qutrit to qubit is realized by the autoencoder. Inspired by the idea of dimensionality reduction, we apply the rotation transformation to compress the states. Starting from Lie algebra, we construct a 3*3 unitary matrix acting on 3*1 state and realize the rotation transformation of the states and then achieve compression of 3*1 state. Each rotation of a state is a compression, and each compression-only needs to adjust two parameters. According to the compression of 3*1 and 4*1 states by unitary matrices, we further discuss the compression law of N*1 states by unitary matrices. In the process of compression, we can adjust the form of the unitary matrix according to the system condition to change the compression position. In this paper, we focus on the compression law along the diagonal from top to bottom. We redesigned the autoencoder and added the waveplate combination to reduce the parameters without affecting the results and achieve the purpose of state compression.
URI: 
ISSN: 
1570-0755 (print)
1573-1332 (online)
DOI: 
Grant ID: 
11647054
11505017
Funding Agency: 
National Natural Science Foundation of China
File Permission: 
None
File Availability: 
No file
Appears in Collections:Journal Articles

Show full item record

SCOPUSTM   
Citations

1
checked on Nov 24, 2022

WEB OF SCIENCETM
Citations

1
checked on Nov 18, 2022

Page view(s)

18
checked on Nov 24, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.