Quantum Competitive Neural Network

Zhou, Ri-Gui

doi:10.1007/s10773-009-0183-y

Cited by 25 publications

(23 citation statements)

References 17 publications

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“…In 2008, the model of quantum BP neural networks based on universal quantum gates is put forth by Li P.C. [6]; In 2009, Panella M., Martinelli G. presented neural networks with quantum architecture and quantum learning [7]; they also pointed out neuro fuzzy networks with nonlinear quantum learning in the same year [8], simultaneously, the authors also designed a few models of quantum neural networks [9][10][11][12], these quantum neural networks (QNN) all make use of a certain quantum characteristic. The nonlinear quantum mechanics was put forth by Abrams D. and Lloyd S. in 1998 [13].…”

mentioning

confidence: 99%

Quantum Associative Neural Network with Nonlinear Search Algorithm

Zhou

Wang

et al. 2011

Int J Theor Phys

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Based on analysis on properties of quantum linear superposition, to overcome the complexity of existing quantum associative memory which was proposed by Ventura, a new storage method for multiply patterns is proposed in this paper by constructing the quantum array with the binary decision diagrams. Also, the adoption of the nonlinear search algorithm increases the pattern recalling speed of this model which has multiply patterns to O(log 2 2 n−t ) = O(n − t) time complexity, where n is the number of quantum bit and t is the quantum information of the t quantum bit. Results of case analysis show that the associative neural network model proposed in this paper based on quantum learning is much better and optimized than other researchers' counterparts both in terms of avoiding the additional qubits or extraordinary initial operators, storing pattern and improving the recalling speed.

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mentioning

confidence: 99%

Quantum Associative Neural Network with Nonlinear Search Algorithm

Zhou

Wang

et al. 2011

Int J Theor Phys

Self Cite

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“…Moreover, the learning algorithm of this model requires to be adapted with threshold parameter depending on the application at hand. The second model, developed by Zhou [20,21] in 2007, established the quantum algorithm that stores a set of predefined patterns in the memory of n neurons. This model increases the storage capacity of the neurons exponentially in contrast to classical neurons.…”

Section: Quantum Competitive Learningmentioning

confidence: 99%

“…The first phase is the storing phase, and is used to store the patterns in the neurons of the competitive ANNs. In the second phase, the competitive phase, Zhou proposed a quantum algorithm that allows the competition between neurons when an input pattern is presented to the network [21]. The primary defect of Zhou's competitive model [21] that the wining pattern is decided with low probability, because this model recalls the wining pattern based on decreased probability of the undesired patterns.…”

Section: Quantum Competitive Learningmentioning

confidence: 99%

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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“…With the rapid development of quantum information, many studies on quantum image representation and algorithm [27][28][29] had been proposed. Among these researches, quantum image representation is a fundamental task, which subtly arranges the digital image into the quantum computer.…”

Section: Quantum Image Representationmentioning

confidence: 99%

Quantum image Gray-code and bit-plane scrambling

Zhou

Sun

Fan

2015

Quantum Inf Process

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With the rapid development of multimedia technology, the image scrambling for information hiding and digital watermarking is crucial. But, in quantum image processing field, the study on image scrambling is still few. Several quantum image scrambling schemes are basically position space scrambling strategies; however, the quantum image scrambling focused on the color space does not exist. Therefore, in this paper, the quantum image Gray-code and bit-plane (GB) scrambling scheme, an entire color space scrambling strategy, is proposed boldly. On the strength of a quantum image representation NEQR, several different quantum scrambling methods using GB knowledge are designed. Not only can they change the histogram distribution of the image dramatically, some designed schemes can almost make the image histogram flush, enhance the anti-attack ability of digital image, but also their cost or complexity is very low. The simulation experiments result also shows a good performance and indicates the particular advantage of GB scrambling in quantum image processing field.

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Quantum Competitive Neural Network

Cited by 25 publications

References 17 publications

Quantum Associative Neural Network with Nonlinear Search Algorithm

Quantum Associative Neural Network with Nonlinear Search Algorithm

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

Quantum image Gray-code and bit-plane scrambling

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