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World Congress on Intelligent Control and Automation

Sunspot number time series prediction using neural networks with quantum gate nodes

Xuezhong Guan, Ligang Sun, Fangfei Yu, Xin Li

Published Jun 1, 2014• 1 citation
Publisher

Abstract

Sunspot is one of the most basic and obvious activity occurred in the solar photosphere, the number of sunspot have great influence on the production and life as well as climate change. Therefore, studying and predicting of sunspots have important meaning. Aiming at the problem of highly nonlinear in sunspot number and the conventional methods are difficult to convergence, a new prediction method based on neural network with quantum gate is proposed to improve the accuracy of the forecast for Sunspot number time series. The input data is expressed by the qubit, which rotated by the rotation gate, as the control qubits control the hidden layer qubits reverse. In the same way, the hidden layer qubits control the output layer. At last, the probability amplitude of state |1> is regarded as the network output. This proposed method has such advantages as high precision and strong generalization ability, and it is a new promising approach for solving time series prediction problem of sunspot number.

DOI
10.1109/wcica.2014.7053498

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Sunspot is one of the most basic and obvious activity occurred in the solar photosphere, the number of sunspot have great influence on the production and life as well as climate change. Therefore, studying and predicting of sunspots have important meaning. Aiming at the problem of highly nonlinear in sunspot number and the conventional methods are difficult to convergence, a new prediction method based on neural network with quantum gate is proposed to improve the accuracy of the forecast for Sunspot number time series. The input data is expressed by the qubit, which rotated by the rotation gate, as the control qubits control the hidden layer qubits reverse. In the same way, the hidden layer qubits control the output layer. At last, the probability amplitude of state |1> is regarded as the network output. This proposed method has such advantages as high precision and strong generalization ability, and it is a new promising approach for solving time series prediction problem of sunspot number.
To enhance the approximation and generalization ability of process neural networks (PNNs), by studying the quantum implementation mechanism of information processing of process neuron, a new idea of designing quantum process neuron is proposed in this paper, based on the quantum rotation gates and the quantum controlled-non gates. In the proposed approach, the discrete process inputs are expressed by the qubits, which, as the control qubits of controlled-non gates after being rotated by the quantum rotation gates, control the target qubits to reverse. The model outputs are described by the probability amplitude of state |1 in the target qubits. Then the quantum process neural networks (QPNNs) are designed by the quantum process neurons for the hidden layer and the normal neurons for the output layer. The algorithm of QPNN is derived through the quantum computing. The proposed approach is utilized to predict the smoothed yearly mean sunspot numbers, and the results indicate that the QPNN has higher prediction accuracy than the normal PNN, thus it has a certain theoretical meaning and practical value for the complex prediction.
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To enhance the approximation and generalization ability of classical artificial neural network (ANN) by employing the principles of quantum computation, a quantum-inspired neuron based on controlled-rotation gate is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-rotation gate after being rotated by the quantum rotation gates, control the target qubit for rotation. The model output is described by the probability amplitude of state |1〉 in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the quantum-inspired neurons to the hidden layer and the classical neurons to the output layer. An algorithm of QNNSI is derived by employing the Levenberg–Marquardt algorithm. Experimental results of some benchmark problems show that, under a certain condition, the QNNSI is obviously superior to the ANN.
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The approximation ability of neural networks plays an important role to the result when one uses neural networks to resolve problems.Unfortunately there is no ideal way to construct proper networks with strong approximation ability.A novel construction approach based on the multi-qubits controller-not gates is proposed for neural networks model in this paper.The proposed model consists of three layers where the hidden nodes are the quantum neurons and the output nodes are the common neurons.The quantum neuron consists of the quantum rotation gates and the multi-qubits controllednot gates.The overall memory of input sequences is captured from information feedback of target qubit from output to input in the multi-qubits controlled-not gate.The output of quantum neuron is obtained from the controlled relationship of the multi-qubits controlled-not gate.The L-M learning algorithms are designed in detail based on the basic principles of quantum computation.The features of input sequences can be effectively obtained in two ways of breadth and depth.The experimental results of solitaire forecast show that,when the number of input nodes is close to the length of sequences,the training set recognition rate of the proposed model increases about 8% than the common neural networks,which reveals the effectiveness of the quantum computation for enhancing the approximation capability of the common neural networks.
Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications.