This paper makes an exhaustive survey of various applications of Quantum inspired computational intelligence (QCI) techniques proposed till date Definition, categorization and motivation for QCI techniques are stated clearly Major Drawbacks and challenges are discussed The significance of this work is that it presents an overview on applications of QCI in solving various problems in engineering, which will be very much useful for researchers on Quantum computing in exploring this upcoming and young discipline

Applications of quantum inspired computational intelligence: a survey

Neural networks have attracted much interest in the last two decades for their potential to realistically describe brain functions, but so far they have failed to provide models that can be simulated in a reasonable time on computers; rather they have been limited to toy models. Quantum computing is a possible candidate for improving the computational efficiency of neural networks. In this framework of quantum computing, the Qubit neuron model, proposed by Matsui and Nishimura, has shown a high efficiency in solving problems such as data compression. Simulations have shown that the Qubit model solves learning problems with significantly improved efficiency as compared to the classical model. In this paper, we confirm our previous results in further detail and investigate what contributes to the efficiency of our model through 4-bit and 6-bit parity check problems, which are known as basic benchmark tests. Our simulations suggest that the improved performance is due to the use of superposition of neural states and the use of probability interpretation in the observation of the output states of the model.

Qubit neural network and its learning efficiency

Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network

Quantum neural network is a useful tool which has seen more development over the years mainly after twentieth century. Like artificial neural network (ANN), a novel, useful and applicable concept has been proposed recently which is known as quantum neural network (QNN). QNN has been developed combining the basics of ANN with quantum computation paradigm which is superior than the traditional ANN. QNN is being used in computer games, function approximation, handling big data etc. Algorithms of QNN are also used in modelling social networks, associative memory devices, and automated control systems etc. Different models of QNN has been proposed by different researchers throughout the world but systematic study of these models have not been done till date. Moreover, application of QNN may also be seen in some of the related research papers. As such, this paper includes different models which have been developed and further the implement of the same in various applications. In order to understand the powerfulness of QNN, few results and reasons are incorporated to show that these new models are more useful and efficient than traditional ANN.

Recent Developments and Applications in Quantum Neural Network: A Review

We have proposed the qubit neuron model as a new scheme in non-standard computing. Identification problems have been investigated on neural networks constructed by this qubit neuron model, and we have found high processing abilities of them. In this paper, we evaluate the performance of the quantum neural network of large size in image compression problems to estimate the utility for the practical applications comparing with the conventional network consists of formal neuron model.

Image Compression by Layered Quantum Neural Networks

Investigations into quantum computations have begun from the pioneering theoretical studies of Feynman, Deutsch, and others, and detailed studies have been done since the discovery of a quantum algorithm which can solve the problem of factorizing a large integer in polynomial time by Shor in 1994. Recently, the existence of nonalgorithmic quantum computations in microtubules inside a neural circuit has been debated, resulting in the proposal of the concept of the quantum neural computation theory, although detailed studies have not as yet been made. In this paper, in order to construct a new framework for describing the cohesiveness of the distribution and synthesis inherent in a neural network, a neural state is described quantum dynamically and a qubitlike neural network corresponding to the quantum circuit of quantum computations is studied. Specifically, a qubitlike neural network is constructed for a 3-bit quantum circuit, which is the minimum quantum logical gate describing all basic logical operations, and in this model we investigate how to determine circuit parameters by learning. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(10): 67–73, 2000

Masato Takai

Papers

A network model based on qubitlike neuron corresponding to quantum circuit

A Learning Network Based on Qubit-Like Neuron Model.