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

Journal of Jishou University

Quantum Neural Computation Technology

Guangjun Xie

Published Jan 1, 2005• 0 citations
OpenAlex

Abstract

Through the comparison between the quantum computation and the neural computation,a new technology called quantum neural computation is presented.A generalized quantum Hebb learning algorithm,a quantum neural network model based on the double-slit interference experiment,and a quantum Hamming competitive learning algorithm are discussed;at the same time some essential characters of the quantum neural computation are also revealed.

Related papers

A Study on Learning with a Quantum Neural Network

M. Kinjo, Shigeo Sato, Kiyotaka Nakajima

A quantum neural network based on the adiabatic quantum computation is one of candidates to overcome the difficulty for developing a quantum computation algorithm. In this paper, we propose a new learning method for a quantum neural network inspired by Hebb learning. Preliminary but successful results by numerical simulations have been shown. The results indicate that a quantum learning like Hebb rule can be implemented.

International Joint Conference on Neural Networks (IJCNN)•Jan 1, 2006•5 citations

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

Research of model of Quantum Learning Vector Quantization Neural Network

Xuan Hou

This paper has been studied in Quantum Learning Vector Quantization Neural Network. The concept of competition and model of competitive network has been studied. It analyses model of Quantum Learning Vector Quantization Neural Network and LVQ algorithm. Quantum Learning Vector Quantization Neural Network is a hybrid neural network and quantum computing theory is used in the Quantum Learning Vector Quantization Neural Network directly to build Quantum Learning Vector Quantization Neural Network. Model of Quantum Learning Vector Quantization Neural Network and Quantum-LVQ algorithms are proposed. In the study of clustering problem, experiments show that Quantum Learning Vector Quantization Neural Network have an advantage over than Learning Vector Quantization Neural Network.

Electronic and Mechanical Engineering and Information Technology•Aug 1, 2011•14 citations

Quantum Neural Networks: Bridging Quantum Computing and Artificial Intelligence

Smrita Singh, Tanzeel Ahmed, Ashutosh Singh Chauhan

Quantum computing and neural networks have revolutionized computational intelligence and have the potential to bring revolution in a wide range of scientific, industrial, and technological fields. In some computational activities, quantum computing utilizes the concepts of superposition, entanglement, and quantum parallelism and performs better than classical computers by processing complicated data with high speed and efficiency. Neural networks have transformed decision-making, natural language processing, and pattern recognition especially deep learning models. By incorporating quantum computing into neural network architectures, Quantum Neural Networks (QNNs) open new paradigms in data encoding, feature representation, and learning dynamics by overcoming the computational constraints and scaling related problems of classical models. In this chapter, the theoretical basis, latest developments and possible applications of a quantum neural network have been discussed. This chapter covers the constraints of near-term quantum devices, quantum noise, data encoding techniques, and practical difficulties in hardware implementation. Real-world applications in quantum chemistry, financial modeling, cryptography, and biological imaging have been studied. To fully realize the promise of quantum neural processing, this chapter concludes by describing current research shortcomings and providing opportunities for multi-disciplinary collaboration among computer scientists, physicists, and AI researchers.

CRC Press eBooks•May 20, 2026•0 citations

Fundamentals of Quantum Computing and Neural Networks

Preeti, Silky

This chapter describes how two transformative paradigms, quantum computing and neural networks, are converging to transform the landscape of intelligent computation. It commences with a description of the basic concepts of quantum mechanics as a computation model, such as qubits, superposition, entanglement, and quantum algorithms, such as Grover and Shor. Then it discusses the neural network structure and learning processes that include its advantages, weaknesses, and its use in the modern artificial intelligence field. Based on these foundations, the chapter presents Quantum Neural Networks (QNNs) and hybrid quantum-classical models, that use quantum phenomena to increase the learning efficiency, optimization, and representational power. A comparison between classical and quantum neural networks reflects the opportunities of the technology, e.g., exponential parallelism, modeling correlations efficiently, the ability to train faster, and the challenges of the NISQ age, such as noise, scalability, and the complexity of optimization. Applications in real world image recognition, drug discovery, finance, and natural language processing are also discussed, as well as future directions including quantum deep learning, fully quantum AI systems, and ethics. This chapter provides a comprehensive perspective of how quantum mechanics and neural computation synergy can result in next-generation intelligent systems through integrated theory, applications, and future vision.

CRC Press eBooks•May 20, 2026•0 citations

A novel Artificial Neural Network training method combined with Quantum Computational Multi-Agent System theory

Xiangping Meng, Jianzhong Wang, Yuzhen Pi +1 more

Artificial Neural Networks (ANNs) are powerful tools that can be used to model and investigate various complex and non-linear phenomena. In this study, we construct a new ANN, which is based on Multi-Agent System (MAS) theory and quantum computing algorithm. All nodes in this new ANN are presented as Quantum Computational (QC) agents, and these agents have learning ability. A novel ANN training method was proposed via implementing QCMAS reinforcement learning. This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm. Experiment results show that this method is effective.

International Journal of Intelligent Systems Technologies and Applications•Jan 1, 2009•1 citation
Through the comparison between the quantum computation and the neural computation,a new technology called quantum neural computation is presented.A generalized quantum Hebb learning algorithm,a quantum neural network model based on the double-slit interference experiment,and a quantum Hamming competitive learning algorithm are discussed;at the same time some essential characters of the quantum neural computation are also revealed.
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.
This paper has been studied in Quantum Learning Vector Quantization Neural Network. The concept of competition and model of competitive network has been studied. It analyses model of Quantum Learning Vector Quantization Neural Network and LVQ algorithm. Quantum Learning Vector Quantization Neural Network is a hybrid neural network and quantum computing theory is used in the Quantum Learning Vector Quantization Neural Network directly to build Quantum Learning Vector Quantization Neural Network. Model of Quantum Learning Vector Quantization Neural Network and Quantum-LVQ algorithms are proposed. In the study of clustering problem, experiments show that Quantum Learning Vector Quantization Neural Network have an advantage over than Learning Vector Quantization Neural Network.
Quantum computing and neural networks have revolutionized computational intelligence and have the potential to bring revolution in a wide range of scientific, industrial, and technological fields. In some computational activities, quantum computing utilizes the concepts of superposition, entanglement, and quantum parallelism and performs better than classical computers by processing complicated data with high speed and efficiency. Neural networks have transformed decision-making, natural language processing, and pattern recognition especially deep learning models. By incorporating quantum computing into neural network architectures, Quantum Neural Networks (QNNs) open new paradigms in data encoding, feature representation, and learning dynamics by overcoming the computational constraints and scaling related problems of classical models. In this chapter, the theoretical basis, latest developments and possible applications of a quantum neural network have been discussed. This chapter covers the constraints of near-term quantum devices, quantum noise, data encoding techniques, and practical difficulties in hardware implementation. Real-world applications in quantum chemistry, financial modeling, cryptography, and biological imaging have been studied. To fully realize the promise of quantum neural processing, this chapter concludes by describing current research shortcomings and providing opportunities for multi-disciplinary collaboration among computer scientists, physicists, and AI researchers.
This chapter describes how two transformative paradigms, quantum computing and neural networks, are converging to transform the landscape of intelligent computation. It commences with a description of the basic concepts of quantum mechanics as a computation model, such as qubits, superposition, entanglement, and quantum algorithms, such as Grover and Shor. Then it discusses the neural network structure and learning processes that include its advantages, weaknesses, and its use in the modern artificial intelligence field. Based on these foundations, the chapter presents Quantum Neural Networks (QNNs) and hybrid quantum-classical models, that use quantum phenomena to increase the learning efficiency, optimization, and representational power. A comparison between classical and quantum neural networks reflects the opportunities of the technology, e.g., exponential parallelism, modeling correlations efficiently, the ability to train faster, and the challenges of the NISQ age, such as noise, scalability, and the complexity of optimization. Applications in real world image recognition, drug discovery, finance, and natural language processing are also discussed, as well as future directions including quantum deep learning, fully quantum AI systems, and ethics. This chapter provides a comprehensive perspective of how quantum mechanics and neural computation synergy can result in next-generation intelligent systems through integrated theory, applications, and future vision.
Artificial Neural Networks (ANNs) are powerful tools that can be used to model and investigate various complex and non-linear phenomena. In this study, we construct a new ANN, which is based on Multi-Agent System (MAS) theory and quantum computing algorithm. All nodes in this new ANN are presented as Quantum Computational (QC) agents, and these agents have learning ability. A novel ANN training method was proposed via implementing QCMAS reinforcement learning. This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm. Experiment results show that this method is effective.