A Quantum Neural Network Approach for Portfolio Selection

Autor:	R. P. Mahajan
Rok vydání:	2011
Předmět:	Quantum neural network Quantitative Biology::Neurons and Cognition Artificial neural network Computer science business.industry Computation Portfolio Artificial intelligence business Greedy algorithm Quantum Quantum computer
Zdroj:	International Journal of Computer Applications. 29:47-54
ISSN:	0975-8887
DOI:	10.5120/3550-4870
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::14f3e8c3b652e2320b2c295cc116d87a https://doi.org/10.5120/3550-4870 Zobrazit plný text záznamu