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

Information technology newsletter

Performance of multiuser detection for CDMA system using quantum neural networks in flat Rayleigh fading environment

Li Fei

Published Jan 1, 2007• 0 citations
OpenAlex

Abstract

A quantum neural networks(QNN) approach for mutiuser detection in CDMA system is theoretically proposed.Quantum neural networks is the combination of quantum computing and artificial neural network(ANN).Taking advantages of the properties of quantum mechanics such as quantum parallelism and entanglement.The computer simulation demonstrates that this algorithm has great advantages of better tolerance to coding error and the far-near effect.These performances of the approach in flat Rayleigh fading environment are also satisfactory.

Related papers

Multiuser Detector Based on Neural Network Quantum Algorithm

Zongsheng Jia

A neural network quantum algorithm(NNQA) that employs a genetic quantum algorithm(GQA) and a Hopfield neural network(HNN) for the multiuser detection problem in code-division multiple-access(CDMA) communications system is proposed.Using this algorithm,the Hopfield neural network is embedded into the GQA to improve further the fitness of the population at each generation.Such a hybridization of the GQA with the Hopfield neural network reduces its computational complexity by providing faster convergence.A better initial data estimation supplied by the GQA improves the performance of the HNN,and the embedded Hopfield neural network improves the performance of the GQA.Simulation results show that the proposed approach of multiuser detection has significant performance improvements over conventional detector and the previous detectors based on intelligent algorithms in terms of bit-error-rate,multiple access interference and near-far resistance.

Jisuanji gongcheng•Jan 1, 2007•0 citations

Feedback Quantum Neuron for Multiuser Detection

Fei Li, Chengjie Xie, Dongsheng Zheng +1 more

Quantum neural network (QNN) is a promising area in the field of quantum computing and quantum information processing. A model of feedback quantum neuron (FQN) is proposed and its property is analyzed. A novel multi-user detector based on FQN and quantum register is proposed. An analysis is given to the theoretical basis and practical performance of the proposed detector. The simulation results show that the proposed detector has more powerful properties both in bit error rate and near-far resistance than Hopfield neural network based detector. A multi-user detector based on improved FQN is also proposed and the simulation results have shown that the improved FQN multi-user detector is obviously superior to the FQN multi-user detector. Its performance is closer to optimal, compared with the other multi-user detector.

International Joint Conference on Neural Networks (IJCNN)•Jan 1, 2006•3 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

Quantum Neural Networks: Evolution of Artificial Neural Networks in Quantum Domain

Ashwani Kumar, Manu Pratap Singh

Quantum Neural Networks (QNNs) represent a fascinating intersection of Quantum Computing and Artificial Neural Networks.Researchers explored combining quantum computing and neural networks in the 1990s and early 2000s.Quantum Neural Networks (QNNs) combine principles from quantum computing and artificial neural networks to potentially revolutionize machine learning.QNNs are neural networks that utilize quantum computing principles, such as superposition, entanglement, and quantum parallelism, to process and transform information.With Quantum Superposition QNNs can represent complex probability distributions, allowing for more nuanced modeling of uncertainty.While with Quantum Entanglement, QNNs can capture complex correlations between inputs, enabling more accurate modeling of complex relationships.Apart of that Quantum Parallelism facilitates QNN to process multiple inputs simultaneously, potentially speeding up certain computations.QNNs can potentially solve certain problems faster than classical neural networks.QNNs can also capture complex relationships and patterns, potentially leading to improved accuracy and can provide new insights into complex systems and phenomena.QNNs hold promise for advancing machine learning and solving complex problems.Ongoing research aims to overcome challenges and unlock their full potential.Quantum-Classical hybrid approaches were introduced, combining the strengths of both paradigms.This research paper presents an exploratory and critical survey of the current status of Quantum Neural Networks, Quantum Computing, along with the potential applications of QNNs in Artificial Intelligence.

International Journal of Scientific and Research Publications•Apr 24, 2026•0 citations

Performance evaluation of quantum based multi-user detector

Sándor Imre, Ferenc Balázs

Future wired and wireless communication systems will employ pure or combined code division multiple access (CDMA) techniques, such as in the European 3G mobile UMTS or power line communications system, but also several 4G proposals include, for example, multicarrier (MC) CDMA. Previous investigations discovered the drawbacks of single user detectors (SUD), which are widely employed in narrowband IS-95 CDMA systems, and were forced to develop suitable multiuser detection schemes to increase the efficiency against interference. However, at this moment there are only suboptimal solutions available became of the rather high complexity of optimal detectors. One of the possible receiver technologies may be quantum assisted computing devices which allow high level parallelism in computation. The first commercial devices are estimated for the next years, which meets the advent of 3G and 4G systems. We analyze the error probability and give tight bounds in a static and dynamically changing environment for a novel quantum computation based quantum multi-user detection (QMUD) algorithm, employing a quantum counting algorithm, which provides an optimal solution.

IEEE International Symposium on Spread Spectrum Techniques and Applications (ISSSTA)•Jun 25, 2003•6 citations

Grover's QSA based MC-CDMA Detector

Saif Hasan Abdulnabi, Saleem Mohammed Ridha Taha

Multi-Carrier Code Division Multiple Access (MC-CDMA) is considered as one of the major techniques used in 4G broadband wireless services.It combines the advantages of the OFDM systems, of robustness to the multi path effects, and the advantages of CDMA systems, which are high privacy and security.However, a problem appears in the detection of transmitted information because of the effect of noise, fading and other multipath effects.In this paper, Grover's quantum search algorithm based MC-CDMA detector is proposed as a solution for this problem.Grover's quantum search algorithm is based on the concepts of quantum computing, such as quantum bit, quantum register and quantum parallelism.The performance of the proposed detector was realized and compared with previous works.The simulation results showed the superiority of the proposed detector in BER performance.The performance of the proposed detector showed that it is very close to the optimum.

International Journal of Computer Applications•Apr 22, 2015•1 citation
A quantum neural networks(QNN) approach for mutiuser detection in CDMA system is theoretically proposed.Quantum neural networks is the combination of quantum computing and artificial neural network(ANN).Taking advantages of the properties of quantum mechanics such as quantum parallelism and entanglement.The computer simulation demonstrates that this algorithm has great advantages of better tolerance to coding error and the far-near effect.These performances of the approach in flat Rayleigh fading environment are also satisfactory.
A neural network quantum algorithm(NNQA) that employs a genetic quantum algorithm(GQA) and a Hopfield neural network(HNN) for the multiuser detection problem in code-division multiple-access(CDMA) communications system is proposed.Using this algorithm,the Hopfield neural network is embedded into the GQA to improve further the fitness of the population at each generation.Such a hybridization of the GQA with the Hopfield neural network reduces its computational complexity by providing faster convergence.A better initial data estimation supplied by the GQA improves the performance of the HNN,and the embedded Hopfield neural network improves the performance of the GQA.Simulation results show that the proposed approach of multiuser detection has significant performance improvements over conventional detector and the previous detectors based on intelligent algorithms in terms of bit-error-rate,multiple access interference and near-far resistance.
Quantum neural network (QNN) is a promising area in the field of quantum computing and quantum information processing. A model of feedback quantum neuron (FQN) is proposed and its property is analyzed. A novel multi-user detector based on FQN and quantum register is proposed. An analysis is given to the theoretical basis and practical performance of the proposed detector. The simulation results show that the proposed detector has more powerful properties both in bit error rate and near-far resistance than Hopfield neural network based detector. A multi-user detector based on improved FQN is also proposed and the simulation results have shown that the improved FQN multi-user detector is obviously superior to the FQN multi-user detector. Its performance is closer to optimal, compared with the other multi-user detector.
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.
Quantum Neural Networks (QNNs) represent a fascinating intersection of Quantum Computing and Artificial Neural Networks.Researchers explored combining quantum computing and neural networks in the 1990s and early 2000s.Quantum Neural Networks (QNNs) combine principles from quantum computing and artificial neural networks to potentially revolutionize machine learning.QNNs are neural networks that utilize quantum computing principles, such as superposition, entanglement, and quantum parallelism, to process and transform information.With Quantum Superposition QNNs can represent complex probability distributions, allowing for more nuanced modeling of uncertainty.While with Quantum Entanglement, QNNs can capture complex correlations between inputs, enabling more accurate modeling of complex relationships.Apart of that Quantum Parallelism facilitates QNN to process multiple inputs simultaneously, potentially speeding up certain computations.QNNs can potentially solve certain problems faster than classical neural networks.QNNs can also capture complex relationships and patterns, potentially leading to improved accuracy and can provide new insights into complex systems and phenomena.QNNs hold promise for advancing machine learning and solving complex problems.Ongoing research aims to overcome challenges and unlock their full potential.Quantum-Classical hybrid approaches were introduced, combining the strengths of both paradigms.This research paper presents an exploratory and critical survey of the current status of Quantum Neural Networks, Quantum Computing, along with the potential applications of QNNs in Artificial Intelligence.
Future wired and wireless communication systems will employ pure or combined code division multiple access (CDMA) techniques, such as in the European 3G mobile UMTS or power line communications system, but also several 4G proposals include, for example, multicarrier (MC) CDMA. Previous investigations discovered the drawbacks of single user detectors (SUD), which are widely employed in narrowband IS-95 CDMA systems, and were forced to develop suitable multiuser detection schemes to increase the efficiency against interference. However, at this moment there are only suboptimal solutions available became of the rather high complexity of optimal detectors. One of the possible receiver technologies may be quantum assisted computing devices which allow high level parallelism in computation. The first commercial devices are estimated for the next years, which meets the advent of 3G and 4G systems. We analyze the error probability and give tight bounds in a static and dynamically changing environment for a novel quantum computation based quantum multi-user detection (QMUD) algorithm, employing a quantum counting algorithm, which provides an optimal solution.
Multi-Carrier Code Division Multiple Access (MC-CDMA) is considered as one of the major techniques used in 4G broadband wireless services.It combines the advantages of the OFDM systems, of robustness to the multi path effects, and the advantages of CDMA systems, which are high privacy and security.However, a problem appears in the detection of transmitted information because of the effect of noise, fading and other multipath effects.In this paper, Grover's quantum search algorithm based MC-CDMA detector is proposed as a solution for this problem.Grover's quantum search algorithm is based on the concepts of quantum computing, such as quantum bit, quantum register and quantum parallelism.The performance of the proposed detector was realized and compared with previous works.The simulation results showed the superiority of the proposed detector in BER performance.The performance of the proposed detector showed that it is very close to the optimum.