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International Journal of Computer Applications

A Quantum Neural Network Approach for Portfolio Selection

R.P Mahajan

Published Sep 29, 2011• 8 citations
Publisher

Abstract

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.

DOI
10.5120/3550-4870

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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.
Quantum Neural Network (QNN) can improve upon the inadequacies of the classical neural network (CNN). The CNN requires a huge memory and needs more computational power. A new field of computation is emerging which integrates quantum computation with CNN. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed. This paper details an approach, perhaps the first attempt; towards stock price prediction using this concept is evolved. The stock price prediction initiates the use of QNN in financial engineering applications
Quantum Neural Network (QNN) can improve upon the inadequacies of the classical neural network (CNN). The CNN requires a huge memory and needs more computational power. A new field of computation is emerging which integrates quantum computation with CNN. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed. This paper details an approach, perhaps the first attempt, towards commodities price prediction using this concept is evolved. The commodity price prediction initiates the use of QNN in financial engineering applications.
This review explores the emerging field of quantum-classical hybrid approaches for predicting stock price movement. Predicting the stock price movement has been a popular topic amongst machine learning (ML) enthusiasts. However, predicting how a particular stock or stock market will perform is a difficult task, due to market dynamics, company (stock) specific dynamics, volatility, and other environmental factors. Using advanced classic machine learning techniques, such predictions have become possible, but also computationally complex. There is a need to develop a non-linear prediction model for predicting the movement of the price of a stock, in a more accurate and faster manner. We examine recent advancements in quantum computing algorithms, focusing on the potential to enhance the efficiency and accuracy of traditional prediction models. This review delves into key concepts such as quantum feature maps, variational quantum circuits, and hybrid architectures that integrate quantum and classical components. We discuss the potential advantages of quantum-enhanced techniques, for their ability to process complex financial data efficiently and unlock hidden patterns. Furthermore, we analyze the current challenges and limitations. This review aims to provide an in-depth overview of the latest quantum-classical stock price prediction, highlighting promising avenues for further research and development in this exciting field.
This review explores the emerging field of quantum-classical hybrid approaches for predicting stock price movement. Predicting the stock price movement has been a popular topic amongst machine learning (ML) enthusiasts. However, predicting how a particular stock or stock market will perform is a difficult task, due to market dynamics, company (stock) specific dynamics, volatility, and other environmental factors. Using advanced classic machine learning techniques, such predictions have become possible, but also computationally complex. There is a need to develop a non-linear prediction model for predicting the movement of the price of a stock, in a more accurate and faster manner. We examine recent advancements in quantum computing algorithms, focusing on the potential to enhance the efficiency and accuracy of traditional prediction models. This review delves into key concepts such as quantum feature maps, variational quantum circuits, and hybrid architectures that integrate quantum and classical components. We discuss the potential advantages of quantum-enhanced techniques, for their ability to process complex financial data efficiently and unlock hidden patterns. Furthermore, we analyze the current challenges and limitations. This review aims to provide an in-depth overview of the latest quantum-classical stock price prediction, highlighting promising avenues for further research and development in this exciting field.
Prediction of stock price remains a challenging task because financial markets are dynamic and often non-linear, unpredictable patterns. To deal with such unpredictable data, the architecture that’s created must identify both time based trends and hidden patterns in data. In this study, we explore a hybrid modelling approach by combining both the classical deep learning techniques with quantum computing principles. Specifically, a Long Short-Term Memory (LSTM) is combined with a Quantum Neural Network (QNN) to understand the benefits of using quantum in time series forecasting. Rather than depending on a single quantum configuration, multiple feature maps and ansatz circuit designs were evaluated to identify the most suitable arrangement for the QLSTM model. Resultant framework blends ideas from both classical computing and quantum approaches to improve how we predict patterns over time in real-life scenarios.