The self-consistent calculation of a spherical quantum dot: A quantum genetic algorithm study

2005 ◽  
Vol 28 (3) ◽  
pp. 247-256 ◽  
Author(s):  
Mehmet Şahin ◽  
Mehmet Tomak
2001 ◽  
Vol 73 (6) ◽  
pp. 749-754 ◽  
Author(s):  
E. Ozturk ◽  
Y. Ergun ◽  
H. Sari ◽  
I. Sokmen

2002 ◽  
Vol 16 (26) ◽  
pp. 3883-3893 ◽  
Author(s):  
MEHMET ŞAHİN ◽  
MEHMET TOMAK

In this study, we have investigated the ground state energy level of electrons in modulation doped GaAs / Al x Ga 1 - x As heterojunctions. For this purpose, Schrödinger and Poisson equations are solved self consistently using quantum genetic algorithm (QGA). In this way, we have found the potential profile, the ground state subband energy and their corresponding envelope functions, Fermi level, and the amount of tunneling charge from barrier to channel region. Their dependence on various device parameters are also examined.


2003 ◽  
Vol 14 (06) ◽  
pp. 775-784 ◽  
Author(s):  
HALUK SAFAK ◽  
MEHMET SAHIN ◽  
BERNA GÜLVEREN ◽  
MEHMET TOMAK

In the present work, genetic algorithm method (GA) is applied to the problem of impurity at the center of a spherical quantum dot for infinite confining potential case. For this purpose, any trial variational wave function is considered for the ground state and energy values are calculated. In applying the GA to the problem under investigation, two different approaches were followed. Furthermore, a standard variational procedure is also performed to determine the energy eigenvalues. The results obtained by all methods are found in satisfactory agreement with each other and also with the exact values in literature. But, it is found that the values obtained by genetic algorithm based upon wavefunction optimization are closer to the exact values than standard variational and also than genetic algorithm based on parameter optimization methods.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Huaixiao Wang ◽  
Jianyong Liu ◽  
Jun Zhi ◽  
Chengqun Fu

To accelerate the evolutionary process and increase the probability to find the optimal solution, the following methods are proposed to improve the conventional quantum genetic algorithm: an improved method to determine the rotating angle, the self-adaptive rotating angle strategy, adding the quantum mutation operation and quantum disaster operation. The efficiency and accuracy to search the optimal solution of the algorithm are greatly improved. Simulation test shows that the improved quantum genetic algorithm is more effective than the conventional quantum genetic algorithm to solve some optimization problems.


Sign in / Sign up

Export Citation Format

Share Document