Quantum Algorithm for Machine Learning and Circuit Design Based on Optimization of Ternary - Input, Binary-Output Kronecker-Reed-Muller Forms

Authors

Maggie Bao, Portland State UniversityFollow
Cole Powers, Portland State UniversityFollow
Marek A. Perkowski, Portland State UniversityFollow

Published In

2021 IEEE 51st International Symposium on Multiple-Valued Logic (ISMVL)

Document Type

Citation

Publication Date

5-25-2021

Abstract

Paper introduces a new spectral expansion of ternary-input binary-output functions that generalizes the binary Kronecker-Reed-Muller forms. Two binary Davio Expansions are generalized to 27 Ternary-Input Davio Expansions. New KRM spectrum has 28n expansions for n ternary variables. By creating an oracle for this problem we generalize the quantum Grover-based algorithms presented for the binary FPRM and KRM forms in the past. Because the method finds solutions also to incompletely specified functions, it can be used to both quantum circuit design and Machine Learning classifier design.

Rights

©2021 IEEE

Locate the Document

https://doi.org/10.1109/ISMVL51352.2021.00029

DOI

10.1109/ISMVL51352.2021.00029

Persistent Identifier

https://archives.pdx.edu/ds/psu/36029

Publisher

IEEE

Citation Details

Bao, M., Powers, C., & Perkowski, M. (2021). Quantum Algorithm for Machine Learning and Circuit Design Based on Optimization of Ternary - Input, Binary-Output Kronecker-Reed-Muller Forms. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ismvl51352.2021.00029