Results 1 
5 of
5
Quantum information processing explanation for interactions between inferences and decisions
 In
, 2007
"... Markov and quantum information processing models are compared with respect to their capability of explaining two different puzzling findings from empirical research on human inference and decision making. Both findings involve a task that requires making an inference about one of two possible uncert ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
Markov and quantum information processing models are compared with respect to their capability of explaining two different puzzling findings from empirical research on human inference and decision making. Both findings involve a task that requires making an inference about one of two possible uncertain states, followed by decision about two possible courses of action. Two conditions are compared: under one condition, the decisions are obtained after discovering or measuring the uncertain state; under another condition, choices are obtained before resolving the uncertainty so that the state remains unknown or unmeasured. Systematic departures from the Markov model are observed, and these deviations are explained as interference effects using the quantum model. Quantum computing and information theory (Neilsen & Chuang, 2000) provides exciting new possibilities for computer science. But what importance does this new theory have for cognitive science? This is a question that is beginning to be asked by an increasing number researchers from a variety of fields including language (Gabora &
Ventura,Training a Quantum Neural network
 http://books.nips.cc/papers/files/nips16/NIPS 2003_ET05.pdf
"... Most proposals for quantum neural networks have skipped over the problem of how to train the networks. The mechanics of quantum computing are different enough from classical computing that the issue of training should be treated in detail. We propose a simple quantum neural network and a training me ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Most proposals for quantum neural networks have skipped over the problem of how to train the networks. The mechanics of quantum computing are different enough from classical computing that the issue of training should be treated in detail. We propose a simple quantum neural network and a training method for it. It can be shown that this algorithm works in quantum systems. Results on several realworld data sets show that this algorithm can train the proposed quantum neural networks, and that it has some advantages over classical learning algorithms. 1
Training a Quantum Neural Network
 http://books.nips.cc/papers/files/nips16/NIPS 2003_ET05.pdf
, 2003
"... Most proposals for quantum neural networks have skipped over the problem of how to train the networks. The mechanics of quantum computing are different enough from classical computing that the issue of training should be treated in detail. We propose a simple quantum neural network and a trainin ..."
Abstract
 Add to MetaCart
Most proposals for quantum neural networks have skipped over the problem of how to train the networks. The mechanics of quantum computing are different enough from classical computing that the issue of training should be treated in detail. We propose a simple quantum neural network and a training method for it. It can be shown that this algorithm works in quantum systems. Results on several realworld data sets show that this algorithm can train the proposed quantum neural networks, and that it has some advantages over classical learning algorithms.
A Quantum Neural Network Approach for Portfolio Selection
"... 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 neur ..."
Abstract
 Add to MetaCart
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 nonlinear formulations that requires huge processing power and large memory which a quantum computer can provide, when they will come into existence.
Artificial Neural Networks Evaluation as an Image Denoising Tool
"... Abstract: Image denoising is a challenging task in the digital image processing research and application. This makes it imperative to find a robust method to comply that task. In this paper, a detailed performance evaluation of using the neural networks as a noise reduction tool is presented. The pr ..."
Abstract
 Add to MetaCart
Abstract: Image denoising is a challenging task in the digital image processing research and application. This makes it imperative to find a robust method to comply that task. In this paper, a detailed performance evaluation of using the neural networks as a noise reduction tool is presented. The proposed approach includes using both mean and median statistical functions for calculating the output pixels of the training pattern of the neural network. This uses part of the degraded image pixel to generate the system training pattern. Different test images, noise levels and neighborhoods sizes are used. Based on using samples of degraded pixel neighborhoods as inputs, the output of proposed approach provided a good image denoising performance, which exhibited promising qualitative and quantitative results of the degraded noisy images in terms of PSNR, MSE and visual tests. Key words: Image denoising Neural networks Pixel neighborhoods Noise variance PSNR MSE