Evolving Quantum Oracles with Hybrid Quantum-inspired
Evolutionary Algorithm
S. Ding, Z. Jin, and Q. Yang. arXiv, (13 October 2008)arXiv:quant-ph/0610105 v1.
Abstract
Quantum oracles play key roles in the studies of
quantum computation and quantum information. But
implementing quantum oracles efficiently with universal
quantum gates is a hard work. Motivated by genetic
programming, this paper proposes a novel approach to
evolve quantum oracles with a hybrid quantum-inspired
evolutionary algorithm. The approach codes quantum
circuits with numerical values and combines the cost
and correctness of quantum circuits into the fitness
function. To speed up the calculation of matrix
multiplication in the evaluation of individuals, a fast
algorithm of matrix multiplication with Kronecker
product is also presented. The experiments show the
validity and the effects of some parameters of the
presented approach. And some characteristics of the
novel approach are discussed too.
1 Institute of Computing Technology, Chinese Academy
of Sciences 2 Academy of Mathematics and Systems
Science, Chinese Academy of Sciences 3 Graduate
University of the Chinese Academy of Sciences Beijing
100080, China 4 School of Computer Science and
Technology, South-Central University for Nationalities,
Wuhan 430074, China
%0 Generic
%1 arXiv:quant-ph/0610105
%A Ding, Shengchao
%A Jin, Zhi
%A Yang, Qing
%D 2008
%K algorithms, genetic programming
%T Evolving Quantum Oracles with Hybrid Quantum-inspired
Evolutionary Algorithm
%U http://arxiv.org/PS_cache/quant-ph/pdf/0610/0610105v1.pdf
%X Quantum oracles play key roles in the studies of
quantum computation and quantum information. But
implementing quantum oracles efficiently with universal
quantum gates is a hard work. Motivated by genetic
programming, this paper proposes a novel approach to
evolve quantum oracles with a hybrid quantum-inspired
evolutionary algorithm. The approach codes quantum
circuits with numerical values and combines the cost
and correctness of quantum circuits into the fitness
function. To speed up the calculation of matrix
multiplication in the evaluation of individuals, a fast
algorithm of matrix multiplication with Kronecker
product is also presented. The experiments show the
validity and the effects of some parameters of the
presented approach. And some characteristics of the
novel approach are discussed too.
@misc{arXiv:quant-ph/0610105,
abstract = {Quantum oracles play key roles in the studies of
quantum computation and quantum information. But
implementing quantum oracles efficiently with universal
quantum gates is a hard work. Motivated by genetic
programming, this paper proposes a novel approach to
evolve quantum oracles with a hybrid quantum-inspired
evolutionary algorithm. The approach codes quantum
circuits with numerical values and combines the cost
and correctness of quantum circuits into the fitness
function. To speed up the calculation of matrix
multiplication in the evaluation of individuals, a fast
algorithm of matrix multiplication with Kronecker
product is also presented. The experiments show the
validity and the effects of some parameters of the
presented approach. And some characteristics of the
novel approach are discussed too.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Ding, Shengchao and Jin, Zhi and Yang, Qing},
biburl = {https://www.bibsonomy.org/bibtex/215a821d1655bc3b1f3745d006743a32c/brazovayeye},
howpublished = {arXiv},
interhash = {66749736b63e1c7e806b417b4f8762d0},
intrahash = {15a821d1655bc3b1f3745d006743a32c},
keywords = {algorithms, genetic programming},
month = {13 October},
note = {arXiv:quant-ph/0610105 v1},
notes = {1 Institute of Computing Technology, Chinese Academy
of Sciences 2 Academy of Mathematics and Systems
Science, Chinese Academy of Sciences 3 Graduate
University of the Chinese Academy of Sciences Beijing
100080, China 4 School of Computer Science and
Technology, South-Central University for Nationalities,
Wuhan 430074, China},
size = {9 pages},
timestamp = {2008-06-19T17:38:46.000+0200},
title = {Evolving Quantum Oracles with Hybrid Quantum-inspired
Evolutionary Algorithm},
url = {http://arxiv.org/PS_cache/quant-ph/pdf/0610/0610105v1.pdf},
year = 2008
}