Recent Developments and Applications in Quantum Neural Network: A Review

Jeswal, Sumit Kumar; Chakraverty, Snehashish

doi:10.1007/s11831-018-9269-0

Cited by 93 publications

(56 citation statements)

References 47 publications

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“…The first attempt to use quantum computing in the classical realm was done by Grover et al [ 32 ] who presented an algorithm to find an element in an unordered dataset quadratically faster than its classical counterpart. At that early time, it was reported that the computational power of the QiNN would be higher than that of ANN in terms of effectiveness and efficiency [ 33 ]. Currently, integrating quantum computation capabilities into ANNs is still an open and challenging trend of research [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

A Novel Autonomous Perceptron Model for Pattern Classification Applications

Sagheer

Zidan

Abdelsamea

2019

Entropy

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Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models.

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Section: Related Workmentioning

confidence: 99%

A Novel Autonomous Perceptron Model for Pattern Classification Applications

Sagheer

Zidan

Abdelsamea

2019

Entropy

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“…Another possible quantum algorithm is quantum neural networks (QNN) [22] which has proven to be applicable in several fields [23]. This too has several similarities including the application of eigenvalue estimation as the key ingredient.…”

Section: Achieving Quantum Speed-up With Hhlmentioning

confidence: 99%

Vibration Analysis of Cyclic Symmetrical Systems by Quantum Algorithms

Mahasinghe

2019

Mathematical Problems in Engineering

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Quantum computers have provided exponentially faster solutions to several physical and engineering problems over existing classical solutions. In this paper we present two quantum algorithms to analyze the forced vibration of a mechanical system with cyclic symmetry. Our main algorithm solves the equation of motion of an undamped and nonelastic rotating system with cyclic symmetry consisting of n sectors, by encoding the displacements of each sector at time t in a quantum state. The runtime of this algorithm is polylog in both n and t, thus exponentially faster than the analogous classical algorithm. Also we consider damped and elastic systems with cyclic symmetry and present another quantum algorithm to solve it in runtime polylog in n and polynomial in t.

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“…In order to overcome the shortcomings of the conventional neural network, a ship steering controller based on the QNN is proposed in this paper under the concept of quantum computing [21][22][23]. e concept of QNN was first proposed by Toth et al in 1996 [24].…”

Section: Introductionmentioning

confidence: 99%

“…Another group of Japanese scholars, Kouda et al, summarized their previous research and summed up an emerging model, which is a quantum neuron model based on general quantum logic gates [26]. In 2018, Jeswal and Chakraverty introduced the latest developments of QNN and discussed the application of QNN [22]. And Xie et al took the general quantum logic gates as the basis function to design a quantum neural computing network, and the simulation results indicated that the QNN is superior to the classical BP neural network and the radial basis function (RBF) neural network computing model in the financial data analysis [27].…”

Section: Introductionmentioning

confidence: 99%

Ship Steering Control Based on Quantum Neural Network

Guan

Zhou

et al. 2019

Complexity

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During the mission at sea, the ship steering control to yaw motions of the intelligent autonomous surface vessel (IASV) is a very challenging task. In this paper, a quantum neural network (QNN) which takes the advantages of learning capabilities and fast learning rate is proposed to act as the foundation feedback control hierarchy module of the IASV planning and control strategy. The numeric simulations had shown that the QNN steering controller could improve the learning rate performance significantly comparing with the conventional neural networks. Furthermore, the numeric and practical steering control experiment of the IASV BAICHUAN has shown a good control performance similar to the conventional PID steering controller and it confirms the feasibility of the QNN steering controller of IASV planning and control engineering applications in the future.

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Recent Developments and Applications in Quantum Neural Network: A Review

Cited by 93 publications

References 47 publications

A Novel Autonomous Perceptron Model for Pattern Classification Applications

A Novel Autonomous Perceptron Model for Pattern Classification Applications

Vibration Analysis of Cyclic Symmetrical Systems by Quantum Algorithms

Ship Steering Control Based on Quantum Neural Network

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