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China Brewing

Quantum neural network and its application in fermentation

Yong Hou

Published Jan 1, 2010• 0 citations
OpenAlex

Abstract

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.

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Abstract Quantum computation is well known for its particular computational performance. In this paper, a novel quantum neuron model is constructed based on the universal quantum gates unit (i.e. phase-shift gate and controlled-NOT gate), which acts as the basic computational component, with this type of neuron a feed-forward network structure is also built. The simulation results show that the quantum neuron network is superior to classical BP and RBF network for two financial data analysis examples.

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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.
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 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.
A quantum neural networks model with learning algorithm is presented. First, 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 prompting. 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 Q-bits. On the basic of gradient descent algorithm, a learning algorithm is proposed. It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting optimum solution. Finally, the availability of the approach is illustrated with application of by adjusting PID controller parameters.
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.
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.
Abstract Quantum computation is well known for its particular computational performance. In this paper, a novel quantum neuron model is constructed based on the universal quantum gates unit (i.e. phase-shift gate and controlled-NOT gate), which acts as the basic computational component, with this type of neuron a feed-forward network structure is also built. The simulation results show that the quantum neuron network is superior to classical BP and RBF network for two financial data analysis examples.