Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

A STUDY OF QUANTUM NEURAL NETWORKS

Telecommunications Nanjing

Published Jan 1, 2003• 0 citations
OpenAlex

Abstract

The study of Quantum Neural Networks (QNN) may give us unprecedented possibilities in creating new systems for information processing. We describe a model for Quantum Neuron(QN) and discuss the quantum learning rule. It is demonstrated that this single quantum neuron is able to perform the XOR function unrealizable with a single classical neuron and has the same computational power as the two-layer perceptron.

Related papers

A study of quantum neural networks

Li Fei, Zheng Baoyu

The study of quantum neural networks (QNN) may give us unprecedented possibilities in creating new systems for information processing. We describe a model for quantum neuron (QN) and discuss the quantum learning rule. It is demonstrated that this single quantum neuron is able to perform the XOR function unrealizable with a single classical neuron and has the same computational power as the two-layer perceptron.

International Conference on Neural Networks and Signal Processing•Jan 1, 2003•16 citations

On the Features of Quantum Neuron

Li Fei

Quantum Neural Networks (QNN) is naturally the next step in the evolution of neuro-computing system. QNN may give us unprecedented possibilities in creating new system for information processing. The fundamentals of QNN are introduced, a model for quantum neuron is described. A quantum learning algorithm is proposed and its convergence property is discussed. The computational power of the quantum neuron is explored. Numerical and graphical results show that this single quantum neuron can perform XOR function unrealizable with a classical neuron and has the same computational power as the two-layer NN, it can be used to realize nonlinear mapping.

Journal of Circuits and Systems•Jan 1, 2004•2 citations

A learning algorithm for quantum neuron

Li Fei, Xiaoliang Dong, Shengmei Zhao +1 more

Most proposals for quantum neural networks (QNN) have skipped over the problem of how to learn the networks. This paper describes a novel model for quantum neuron and proposes its learning algorithm. It can be shown that this algorithm works on quantum systems and the convergence result demonstrates efficient performance of the proposed quantum neuron. Numerical and graphical results show that this single quantum neuron can perform the XOR function unrealizable with a classical neuron and can eliminate the necessity of building a network of neurons to obtain nonlinear mapping.

International Conference on Signal Processing•Jun 28, 2005•4 citations

Computational Power of Quantum Artificial Neural Network

Sitakanta Nayak, Shaktikanta Nayak

Quantum Artificial Neural Network (QANN) is one of the new paradigm built upon the combination of classical neural computation and quantum computation. It has great values for theoretic study and potential to applications. In this paper an attempt is made to show how a single quantum neuron is able to perform the XOR function which is unrealizable with a single classical neuron and has the same computational power as the two-layer perceptron.

•Jan 1, 2011•1 citation

AN INTRODUCTION TO QUANTUM NEURAL COMPUTING

Shaktikanta Nayak, Sitakanta Nayak, Jaskaran Singh

The goal of the artificial neural network is to create powerful artificial problem solving systems. The field of quantum computation applies ideas from quantum mechanics to the study of computation and has made interesting progress. Quantum Neural Network (QNN) is one of the new paradigms built upon the combination of classical neural computation and quantum computation. It is argued that the study of QNN may explain the brain functionality in a better way and create new systems for information processing including solving some classically intractable problems. In this paper we have given an introductory representation of quantum artificial neural network to show how it can be modelled on the basis of double-slit experiment. Also an attempt is made to show the quantum mechanical representation of a classical neuron to implement Hadamard transformation.

Journal of Global Research in Computer Sciences•Sep 14, 2011•8 citations

Efficient learning algorithm for quantum perceptron unitary weights

Kok-Leong Seow, Elizabeth Behrman, James E. Steck

For the past two decades, researchers have attempted to create a Quantum Neural Network (QNN) by combining the merits of quantum computing and neural computing. In order to exploit the advantages of the two prolific fields, the QNN must meet the non-trivial task of integrating the unitary dynamics of quantum computing and the dissipative dynamics of neural computing. At the core of quantum computing and neural computing lies the qubit and perceptron, respectively. We see that past implementations of the quantum perceptron model have failed to fuse the two elegantly. This was due to a slow learning rule and a disregard for the unitary requirement. In this paper, we present a quantum perceptron that can compute functions uncomputable by the classical perceptron while analytically solving for parameters and preserving the unitary and dissipative requirements.

arXiv:1512.00522•Dec 1, 2015•3 citations
The study of Quantum Neural Networks (QNN) may give us unprecedented possibilities in creating new systems for information processing. We describe a model for Quantum Neuron(QN) and discuss the quantum learning rule. It is demonstrated that this single quantum neuron is able to perform the XOR function unrealizable with a single classical neuron and has the same computational power as the two-layer perceptron.
Quantum Neural Networks (QNN) is naturally the next step in the evolution of neuro-computing system. QNN may give us unprecedented possibilities in creating new system for information processing. The fundamentals of QNN are introduced, a model for quantum neuron is described. A quantum learning algorithm is proposed and its convergence property is discussed. The computational power of the quantum neuron is explored. Numerical and graphical results show that this single quantum neuron can perform XOR function unrealizable with a classical neuron and has the same computational power as the two-layer NN, it can be used to realize nonlinear mapping.
Most proposals for quantum neural networks (QNN) have skipped over the problem of how to learn the networks. This paper describes a novel model for quantum neuron and proposes its learning algorithm. It can be shown that this algorithm works on quantum systems and the convergence result demonstrates efficient performance of the proposed quantum neuron. Numerical and graphical results show that this single quantum neuron can perform the XOR function unrealizable with a classical neuron and can eliminate the necessity of building a network of neurons to obtain nonlinear mapping.
The goal of the artificial neural network is to create powerful artificial problem solving systems. The field of quantum computation applies ideas from quantum mechanics to the study of computation and has made interesting progress. Quantum Neural Network (QNN) is one of the new paradigms built upon the combination of classical neural computation and quantum computation. It is argued that the study of QNN may explain the brain functionality in a better way and create new systems for information processing including solving some classically intractable problems. In this paper we have given an introductory representation of quantum artificial neural network to show how it can be modelled on the basis of double-slit experiment. Also an attempt is made to show the quantum mechanical representation of a classical neuron to implement Hadamard transformation.
For the past two decades, researchers have attempted to create a Quantum Neural Network (QNN) by combining the merits of quantum computing and neural computing. In order to exploit the advantages of the two prolific fields, the QNN must meet the non-trivial task of integrating the unitary dynamics of quantum computing and the dissipative dynamics of neural computing. At the core of quantum computing and neural computing lies the qubit and perceptron, respectively. We see that past implementations of the quantum perceptron model have failed to fuse the two elegantly. This was due to a slow learning rule and a disregard for the unitary requirement. In this paper, we present a quantum perceptron that can compute functions uncomputable by the classical perceptron while analytically solving for parameters and preserving the unitary and dissipative requirements.