Keywords
quantum state pairs, quantum algorithms, classical machine learning
Abstract
Developing quantum algorithms has proven to be very difficult. In this paper, the concept of using classical machine learning techniques to derive quantum operators from examples is presented. A gradient descent algorithm for learning unitary operators from quantum state pairs is developed as a starting point to aid in developing quantum algorithms. The algorithm is used to learn the quantum Fourier transform, an underconstrained two-bit function, and Grover’s iterate.
Original Publication Citation
Neil Toronto and Dan Ventura, "Learning Quantum Operators from Quantum State Pairs", Proceedings of the IEEE Congress on Evolutionary Computation, pp. 9157-9162, July 26.
BYU ScholarsArchive Citation
Toronto, Neil and Ventura, Dan A., "Learning Quantum Operators From Quantum State Pairs" (2006). Faculty Publications. 307.
https://scholarsarchive.byu.edu/facpub/307
Document Type
Peer-Reviewed Article
Publication Date
2006-07-01
Permanent URL
http://hdl.lib.byu.edu/1877/2554
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/