In the framework of quantum theory,the process of the neural unit dealing with information is explained,and quantum neural unit is proposed.The process includes two stages.The first stage is collecting macroscopical information,in which control quantum bit is produced.The second stage is dealing microcosmic information that neural unit state is changed according to control quantum bit.The whole process simulates the quantum controlled NOT gate.The synthetic datasets and real datasets are used as classification objects.Classification results of the quantum neural network are better than that of the classical neural network.Additionally,acrylonitrile reactor is used as modeling object.The proposed network shows the better performance.
In the framework of quantum theory,the process of the neural unit dealing with information is explained,and quantum neural unit is proposed.The process includes two stages.The first stage is collecting macroscopical information,in which control quantum bit is produced.The second stage is dealing microcosmic information that neural unit state is changed according to control quantum bit.The whole process simulates the quantum controlled NOT gate.The synthetic datasets and real datasets are used as classification objects.Classification results of the quantum neural network are better than that of the classical neural network.Additionally,acrylonitrile reactor is used as modeling object.The proposed network shows the better performance.
A quantum neural network to simulate quantum controlled NOT gate was presented in the study.At the first stage,the neural cell collected macroscopical information of samples and generated controlled quantum bit.The second stage,was named quantum controlled NOT gate simulation, which means the quantum neural cell will be changed according to the corresponding control of quantum bit.Quantum neural network with quantum neural cell is applied in bacterium growth model of Verhulst equation for parameters estimation of fermentation model for glutamic acid production. It is showed that the method of parameters estimation based on quantum neural network has the advantages of high precision,feasible program and low computation.
A quantum neural networks model and its learning algorithm are presented.Firstly,a quantum controlled-rotating gate is proposed by analyzing the meaning of the controlled-NOT gate.Then a quantum neuron model is constructed from the physical meaning of the controlled rotating gate.The model includes two kinds of design parameters,rotation angle and its control range.Secondly,a quantum neural networks model based on quantum neuron is proposed.By using gradient descent algorithm,a learning algorithm of this model is designed in detail.With applications of pattern recognition and time series prediction,the simulation results show that the proposed algorithm is superior to the common BP neural networks in both convergence capability and robustness.
Generalization is an important feature of neural network, and there have been many studies on it. Recently, with the development of quantum compu-ting, it brings new opportunities. In this paper, we studied a class of quantum neural network constructed by quantum gate. In this model, we mapped the feature data to a quantum state in Hilbert space firstly, and then implement unitary evolution on it, in the end, we can get the classification result by im-plement measurement on the quantum state. Since all the operations in quan-tum neural networks are unitary, the parameters constitute a hypersphere of Hilbert space. Compared with traditional neural network, the parameter space is flatter. Therefore, it is not easy to fall into local optimum, which means the quantum neural networks have better generalization. In order to validate our proposal, we evaluated our model on three public datasets, the results demonstrated that our model has better generalization than the classical neu-ral network with the same structure.
To address the integration of quantum computing and neural computing, this paper investigates a quantum neural network (QNN) model and its algorithm based on swap test and phase estimation. Firstly, a quantum circuit for swap tests with multi-qubit control is proposed, and a quantum neuron model is developed by combining this with phase estimation. The model uses quantum bits for inputs, weights, and outputs, where the phase of the weight qubits serves as the model parameters. Based on this quantum neuron, a QNN model is constructed, with measurements performed at the network output to obtain real-valued outputs. Various quantum circuits associated with the network model are designed in detail, and the input-output relationships for each network layer are derived according to quantum computing theory. Additionally, a method for adjusting network parameters based on the gradient descent algorithm is thoroughly developed. Finally, simulations are conducted on a classical computer using planar point set recognition and handwritten digit binary classification. Although these simulations do not verify the parallelism of quantum computing, they do validate the model??s execution effectiveness. The results show that the proposed model has a clear advantage in classification performance compared with a classical backpropagation (BP) neural network with the same parameter scale. This demonstrates that constructing QNN models based on multi-qubit swap tests and phase estimation is both effective and feasible, offering a new perspective for future research on quantum neural networks.
Quantum computing is described as a process by which a system calculates output. Quantum physics usually refers to the smallest discrete unit of any property; the basic unit of data in quantum is the qubit; this qubit unit is equivalent to the bit unit in classical neural networks. Quantum deep learning combines quantum computing with deep learning to reduce training time for neural networks, which has proven effective in solving some intractable problems on classical computers. Quantum deep learning has proven effective in solving some intractable problems on classical computers. A quantum network can benefit from quantum informationflow because it is a more efficient framework than classical systems. Each quantum deep learning consists of a quantum gate. In this review, we provide a comprehensive review of recent studies that include different quantum deep learning applications, including healthcare, handwriting, and many others. Also, methodologies, problems, main datasets, results, strengths, limitations, and challenges are included in this review.
This paper investigates a quantum neural network and discusses its application in control systems. A learning-type neural network-based controller that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. Three learning algorithms; a back-propagation algorithm, a conjugate gradient algorithm and a real-coded genetic algorithm, are investigated to supervise the training of the multi-layer quantum neural network. To evaluate the learning performance and the capability of the quantum neural network-based controller, we conducted computational experiments for controlling a nonlinear discrete-time plant and a nonholonomic system - in this study a two-wheeled robot. The results of computational experiments confirm both the feasibility and the effectiveness of the quantum neural network-based controller and that the real-coded genetic algorithm is suitable for the learning method of the quantum neural network-based controller.