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Information Science and Management Engineering

A New Method of Image Compression Based on Quantum Neural Network

Huifang Li, Mo Li

Published Aug 1, 2010• 19 citations
Publisher

Abstract

In this paper we combine with quantum neural networks and image compression using Quantum Gates as the basic unit of quantum computing neuron model, and establish a three layer Quantum Back Propagation Network model, then the model is used for realizing image compression and reconstruction. Since the initial weights of neural networks were slow convergence, we use Genetic Algorithm (GA) to optimize the neural network weights, and present a mechanism called clamping to improve the genetic algorithm. Finally, we combined the Genetic Algorithm with quantum neural networks to finish image compression. Through an experiment we can see the superiority of the improved algorithm.

DOI
10.1109/isme.2010.242

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In this paper we combine with quantum neural networks and image compression using Quantum Gates as the basic unit of quantum computing neuron model, and establish a three layer Quantum Back Propagation Network model, then the model is used for realizing image compression and reconstruction. Since the initial weights of neural networks were slow convergence, we use Genetic Algorithm (GA) to optimize the neural network weights, and present a mechanism called clamping to improve the genetic algorithm. Finally, we combined the Genetic Algorithm with quantum neural networks to finish image compression. Through an experiment we can see the superiority of the improved algorithm.
Quantum Neural Network (QNN), which integrates the characteristics of Artificial Neural Network (ANN) with quantum theory, is a new study field. It takes advantages of ANN and quantum computing and has a high theoretical value and potential applications. Based on quantum neuron model with a quantum input and output quantum and artificial neural network theory, at the same time, QBP algorithm is proposed on the basis of the complex BP algorithm, the network of a 3-layer quantum BP which implements image compression and image reconstruction is built. The simulation results show that QBP can obtain the reconstructed images with better quantity compared with BP in spite of the less learning iterations.
In order to improve the quality of image restoration, a quantum 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 neurons, and the output layer consists of classical 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 by way of “breadth” and “depth”. The simulation results show that the quality of image restoration of proposed model is obviously superior to that of classical artificial neural network.
The paper presents the development and research of a supervised quantum neural network algorithm designed to perform image compression and restoration tasks. The proposed technique combines the capabilities of quantum computing with neural network approaches, which ensures the efficient use of quantum parallelism, superposition and entanglement. A mathematical model of a quantum neuron and a learning algorithm based on the Widrow-Hoff method adapted to quantum architecture are described. The quantum frameworks Qiskit and Pennylane are used to build and test the model. The experimental results confirm the ability of the model to achieve a high degree of image compression with minimal loss of quality, as well as to ensure accurate restoration. The data obtained indicate a significant potential of quantum neural networks in the tasks of intelligent processing of visual information.
A novel neural networks model with quantum weights is present.Firstly, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and inspiriting.Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors; the linked weight and the activation value are qubits.On the basic of the gradient descent algorithm, a learning algorithm of this model is proposed.It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting the global optimal solution.Finally, the availability of the model is illustrated in both convergence speed and convergence rate by two application examples of pattern recognition and function approximation.
Abstract Using global optimization algorithm to optimize the initial weights and thresholds of traditional neural network model can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slower convergence speed and poor local search ability still exist. In order to solve these problems, a neural network model QGA-QGCNN using a Quantum Genetic Algorithm (QGA) to optimize Quantum Gate Circuit Neural Network (QGCNN) is proposed in this paper. In QGA-QGCNN, the initial parameters of QGCNN are optimized for the strong global optimization ability and faster convergence speed by using a QGA. In the proposed model, the normalized results of the predicted sample attributes are transformed into quantum states as the input of the network, and the quantum rotation gate is used to rotate the phase and control the reversal of the target qubits. Finally, the output of the network is obtained by entanglement of multiple qubits in the quantum Controlled-NOT (CNOT) gate. When dealing with more complex problems, the QGCNN model based on quantum computing has specific parallel computing capabilities and can give full play to its ability to blur uncertain problems, thereby improving detection performance. We use the authoritative 10% KDD-CUP99 data set in the field of network intrusion detection to conduct simulation experiments on the proposed QGA-QGCNN model. Experimental results show that the proposed intrusion detection model has lower false alarm rate and significantly accuracy compared to conventional attack detection models. And GQCNN optimized by QGA also improves the convergence performance of the model.
A quantum neural networks model based on quantum neurons and traditional neurons is presented in this paper. The input of quantum neuron is real vector, its weight is quantum bits, its transforming function is an inner product operator and its output is a real number. The network includes three layers. Input layer is composed of traditional neurons that receive input information. Hidden layer is composed of quantum neurons that extract pattern feature of input information and transfer them to output layer. Output layer is composed of traditional neurons that export calculation result. The weightings of output layer are rectified by back propagation algorithm. The weightings of hidden layer are rectified by a group of quantum gates. A detailed learning algorithm is designed. Finally the availability of the model and algorithm is illustrated by two application examples of pattern recognition and functional approximation.