DocumentCode
2465887
Title
Learning Quantum Operators From Quantum State Pairs
Author
Toronto, Neil ; Ventura, Dan
Author_Institution
Brigham Young Univ., Provo
fYear
0
fDate
0-0 0
Firstpage
2607
Lastpage
2612
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.
Keywords
Fourier transforms; gradient methods; learning (artificial intelligence); mathematical operators; quantum computing; gradient descent algorithm; machine learning; quantum Fourier transform; quantum algorithms; quantum state pairs; unitary operator learning; Convergence; Fourier transforms; Genetic algorithms; Iterative algorithms; Iterative methods; Machine learning; Machine learning algorithms; Quantum computing; Quantum entanglement; Solids;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9487-9
Type
conf
DOI
10.1109/CEC.2006.1688634
Filename
1688634
Link To Document