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International Conference on Neural Networks and Brain

Feedback Quantum Neuron and Its Application

Fei Li, Shengmei Zhao, Baoyu Zheng

Published Apr 28, 2006• 3 citations
Publisher

Abstract

A model of feedback quantum neuron (FQN) is proposed. A novel multi-user detector based on feedback quantum neuron 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 close to the optimum multi-user detector.

DOI
10.1109/icnnb.2005.1614759

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To enhance the approximation capability of neural networks, a quantum neural networks model is proposed whose input of each dimension is in discrete sequence. This model includes three layers, in which the hidden layer consists of quantum neurons, and the output layer consists of common neurons. The quantum neuron consists of the quantum rotation gates and the multi-qubits controlled-not gates. By using the information feedback of target qubit from output to input in multi-qubits controlled-not gate, the overall memory of input sequences is realized. The output of quantum neuron is obtained from the entanglements of multi-qubits in controlled-not gates. The learning algorithm is designed in detail according to the basis principles of quantum computation. The characteristics of input sequence can be effectively obtained from the width and the depth. The simulation results show that, when the input nodes and the length of the sequence satisfy a certain relations, the proposed model is superior to the common artificial neural networks.
To enhance the approximation and generalization ability of classical artificial neural networks,a quantum-inspired neural network model,whose input of each dimension is a discrete sequence,is proposed.This model concludes three layers,in which the hidden layer consists of quantum-inspired neurons,and the output layer consists of common neurons.The quantum-inspired neuron consists of the quantum rotation gates and the multi-qubits controlled-rotation gates.By using the information feedback of target qubit from output to input in multi-qubits controlled-rotation gate,the overall memory of input sequences is realized.The output of quantum-inspired neuron is obtained from the entanglements of multi-qubits in controlled-rotation gates.The learning algorithm is designed in detail according to the basic principles of quantum computation.The characteristics of input sequence can be effectively obtained by way ofbreadthanddepth.The simulation results show that,when the input nodes and the length of the sequence satisfy a certain relations,the proposed model is obviously superior to the common artificial neural networks.
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 genetic algorithm (QGA) is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Instead of binary, numeric, or symbolic representation, QGA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. A novel multi-user detector based on QGA 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 in bit error rate than conventional genetic algorithm based detector, namely a 2-3 dB gain in performance can be achieved. The results demonstrated the effectiveness and the applicability of QGA in multi-user detection.
The optimal solution of MIMO signal detection is a NP problem.Making use of the properties of quantum mechanics such as quantum parallelism and entanglement,Quantum Neural network(QNN) can solve this problem efficiently.This paper adopts method of Hopfield neural network to realize MIMO signals detection.Basic on mapping between detecting sequence Maximum a posteriori Probality(MAP) optimum reception likelihood function and energy function of Hopfield nerve net,we conformat a kind of quantum neural network MIMO singal detector.Computer simulation results show that the method has powerful property in bit error rate.