Quantum artificial neural network architectures and components

Narayanan, Ajit; Menneer, Tammy

doi:10.1016/s0020-0255(00)00055-4

Cited by 153 publications

(72 citation statements)

References 6 publications

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“…Such modification leads to variate of neural network paradigms such as polynomial neural network [233,244], where the nodes are designed to as a polynomial function based on inputs to the nodes. Similarly, the nodes of a GMDH neural network is designed as an Ivakhnenko polynomial [245]; the nodes of a complex value neural network or multivalued neural network is designed with a complex value activation functions [234]; the node of spiking neural networks has specific behavior, in which a node signal is propagated to another node only if the intrinsic quality of neural activation value is above a defined threshold [246]; the nodes of fuzzy neural network paradigm is designed using the concepts of fuzzy theory [247]; the node and the architecture of the Quantum neural network are inspired by the quantum computing [248][249][250][251]. In all such methods, metaheuristics have a significant role in the optimization.…”

Section: Node Optimizationmentioning

confidence: 99%

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Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

457

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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Section: Node Optimizationmentioning

confidence: 99%

“…At the first place, the QNN as a quantum perceptron was proposed by Lewestein [276], where instead of classical weights, a unitary operator was used to map inputs to an output. The study in QNN encompasses the development of quantum weights, quantum neurons, quantum network, and quantum learning [249].…”

Section: Combination Of Fnn Components Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

457

154

View full text Add to dashboard Cite

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“…And the objective quantum register |ob j used to mark configurations with desired performance. A configuration of the weightless neuron during the execution of Algorithm 1 will be represented using the quantum state |ψ described in Equation (14). |ψ = |i |s |o |d |perf |ob j…”

Section: Non Linear Quantum Learningmentioning

confidence: 99%

“…Attempts to bring quantum computing power to a greater range of problems are the development of quantum machine-learning algorithms as decision trees [6], evolutionary algorithms [7] and artificial neural networks [8,9,10,11,12,13,14,15,16]. In this paper, we are concerned in the field of quantum weightless neural networks.…”

Section: Introductionmentioning

confidence: 99%

Weightless neural network parameters and architecture selection in a quantum computer

2016

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Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques have been proposed to automatise the architecture selection process. In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN). The architecture selection is performed through the learning procedure of a qWNN with a learning algorithm that uses the principle of quantum superposition and a non-linear quantum operator. The main advantage of the proposed method is that it performs a global search in the space of qWNN architecture and parameters rather than a local search.

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“…Gupta and Gia [9] has shown that QNN has almost the same computational power as CNN. Menneer and Narayan [10,11] has laid some foundation of basic concepts inspired by quantum theory for use in neural networks design, development and implementation. It has been further argued that, a fully QNN has no advantage over a hybrid QNN and may produce worst results.…”

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 artificial neural network architectures and components

Cited by 153 publications

References 6 publications

Metaheuristic design of feedforward neural networks: A review of two decades of research

Metaheuristic design of feedforward neural networks: A review of two decades of research

Weightless neural network parameters and architecture selection in a quantum computer

A Quantum Neural Network Approach for Portfolio Selection

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