Hybrid Quantum Neural Networks Model Algorithm and Simulation

Hong, Xia; Cao, Maojun

doi:10.1109/icnc.2009.128

Cited by 5 publications

(11 citation statements)

References 6 publications

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“…As such, the proposed algorithm combines the computation power of QNN with nonlinear capability of traditional ANN. Combining quantum neurons in the first layer with classical neurons in the second layer yields what is known as hybrid quantum neural network (HQNN) [9]. In contrast, general QNN approaches should include quantum neurons only.…”

Section: B Quantum-competitive Phasementioning

confidence: 99%

“…Toward more evaluation for the proposed algorithm, we testify it to achieve classification purpose in a reported pattern classification experiment [9]. The experiment includes the distribution of nine points on the plane; see Fig.…”

Section: B Pattern Classification Experimentsmentioning

confidence: 99%

“…2, where the task is to determine the class of each point. This experiment is a typical two-pattern classification problem, which is regard as the generalization of the known XOR problem, where each point represents a pattern [9]. shapes corresponds to each point in Fig.…”

Section: B Pattern Classification Experimentsmentioning

confidence: 99%

“…One important motivation for us for this investigation is the difficulty to train CNNs, low capacity of patterns storage, and complex computation, especially in case of big data applications. The hope is that features of quantum computing such as quantum parallelism or huge storage capacities or the effects of interference and entanglement can be used as resources to yield autonomous CL algorithms based on quantum theory [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An autonomous competitive learning algorithm using quantum hamming neural networks

Zidan

Sagheer

Metwally

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Quantum neural network is a fledging research domain based on the merge of classical neural network and quantum computing. In this paper, we add the quantum effect to the classical hamming neural network algorithm in order to employ the advantages of quantum information to yield, finally, a novel quantum competitive learning algorithm. The proposed algorithm, called quantum hamming neural network (QHNN), is capable to recognize incomplete patterns, as well as, increase the probability of recognizing patterns on the account of undesired patterns. Moreover, these undesired patterns could be used as new patterns for training the algorithm in subsequent steps. The proposed algorithm is testified via a case study and a classification experiment, where promising results, reaches to 100%, are given and compared favorably with other reported quantum competitive algorithms.

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Section: B Quantum-competitive Phasementioning

confidence: 99%

Section: B Pattern Classification Experimentsmentioning

confidence: 99%

Section: B Pattern Classification Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An autonomous competitive learning algorithm using quantum hamming neural networks

Zidan

Sagheer

Metwally

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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“…This combination can produce a computational paradigm of incredible potential. Xiao and Cao [13] have proposed a QNN model based on quantum and classical neurons. Miszczak [14] has proposed ways for preparing initial quantum state based on probabilities and Nielsen et.…”

Section: Introductionmentioning

confidence: 99%

A Quantum Neural Network Approach for Portfolio Selection

Mahajan¹

2011

IJCA

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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. KeywordsQuantum neural network, portfolio selection, resource allocation, stock price prediction, investment weights, quantum back propagation, quantum computation.

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Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure

et al. 2019

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In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.

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Hybrid Quantum Neural Networks Model Algorithm and Simulation

Cited by 5 publications

References 6 publications

An autonomous competitive learning algorithm using quantum hamming neural networks

An autonomous competitive learning algorithm using quantum hamming neural networks

A Quantum Neural Network Approach for Portfolio Selection

Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure

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