CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

A Quantum Neural Network Approach for Portfolio Selection

by R.P Mahajan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 4
Year of Publication: 2011
Authors: R.P Mahajan

R.P Mahajan . A Quantum Neural Network Approach for Portfolio Selection. International Journal of Computer Applications. 29, 4 ( September 2011), 47-54. DOI=10.5120/3550-4870

@article{ 10.5120/3550-4870,
author = { R.P Mahajan },
title = { A Quantum Neural Network Approach for Portfolio Selection },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 4 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 47-54 },
numpages = {9},
url = { },
doi = { 10.5120/3550-4870 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:14:56.766007+05:30
%A R.P Mahajan
%T A Quantum Neural Network Approach for Portfolio Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 4
%P 47-54
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

A new field of computation is emerging which integrates quantum and classical computation. This is applied to solve the financial engineering problem of portfolio selection. Hopfield neural network is used for portfolio selection. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed for the prediction of stock prices. An effort is made, probably the first time to develop and use a hybrid quantum neural network for the prediction of stock prices. The suggested multilayer hybrid quantum neural network contains hidden layer of quantum neurons while the visible layer is of classical neurons. The asset distribution is done by a modified greedy algorithm. It is assumed that quantum computers when come into existence shall provide huge potential in the form of computational power and memory. Classical Neural networks(CNN) have shown tremendous acceptability in solving problems with non-linear formulations that requires huge processing power and large memory which a quantum computer can provide, when they will come into existence.

  1. H. Markowitz, “Portfolio Selection”, The Journal of Finance, Vol. 7, No. 1. (Mar., 1952), pp. 77-91.
  2. TJ Chang et. al, “Heuristic for Heuristics for cardinality constrained portfolio optimization”, Computer and Operations Research 27(2000) 1271-1302.
  3. Alberto Fernández, Sergio Gómez, “Portfolio selection using neural networks”,,,2005
  4. P. Benioff, “The computer as physical system: A microscopic Quantum Mechanical Hamiltonian model of computers as represented by turing machine,” Journal of Statistical Physics, vol. 22, pp. 563-591, 1980.
  5. R. Feynman, “Simulating physics with computers”, International Journal of Theoretical. Physics, 21, pp 467-488, 1982
  6. Shor, Peter W., “Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer”, SIAM Journal on Computing, Vol. 26, pp. 1484 – 1509, October 1997.
  7. Lov K. Grover., “A fast quantum mechanical algorithm for database search”, In Proceedings of the 28th ACM STOC, pages 212–219, 1996.
  8. SC Kak, “Quantum neural computing”, Advances in Imaging and Electron Physics 94, pp. 259-314, 1995.
  9. S. Gupta and R. Zia, “Quantum neural networks”, Technical report, Available: /quant- ph/ pdf/ 0201/ 0201144.pdf, 2002.
  10. T. Menneer, “Quantum Artificial Neural Networks”, PhD thesis, University of Exeter, 1998.
  11. Ajit Narayanan and Tammy Menneer, “Quantum artificial neural network architectures and components.”, Information Sciences, volume 124 nos. 1-4, pages 231–255, 2000.
  12. Ezhov, A. and D. Ventura, “Quantum neural networks”, in: N. Kasabov(ed) Future Directions for Intelligent Systems and Information Sciences, Springer Verlag 2000.
  13. Hong Xiao and M. Cao, “Hybrid quantum neural networks model algorithm and simulation” in the proceedings of the fifth International Conference on Neural Computation Tiaingiin, china, 2009.
  14. J. A. Miszczak. “Initialisation of quantum registers based on probability distribution.” Technical report, Available: IITiS PAN, 2007.
  15. Michael A. Nielsen and Isaac L. Chuang, “Quantum computation and quantum information”, Cambridge University Press, 2000.
  16. Everett H., “The theory of the universal wave function. In N. Graham & B. DeWitt (Eds.)”, The many-worlds interpretation of Quantum Mechanics (3-140). Princeton: Princeton University Press (1973).
  17. Gui Lu Long, Li Xiao, “Parallel Quantum Computing in a Single Ensemble Quantum Computer”, Physical Review A 69, 052303 (2004)
  18. Juliana Kaizer Vizzotto and André Rauber Du Bois, Modeling Parallel Quantum Computing Using Transactional Memory, Electronic Notes in Theoretical Computer Science, Volume 270, Issue 1, 10 February 2011, Pages 183-190
  19. FDD Freitas et. al, “Portfolio Selection with Predicted Returns Using Neural Networks”, ACM-DL , Expert System with Applications vol 35 issue 1-2, July 2008
  20. BSE(Bombay Stock Exchange).
Index Terms

Computer Science
Information Sciences


Quantum neural network portfolio selection resource allocation stock price prediction investment weights quantum back propagation quantum computation