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International Conference on Information Technology in Medicine and Education

Disease Classification Model Based on Qubit Neural Tree Networks

Qi Feng, Qimei Wang, Lili Xu

Published Oct 1, 2018• 1 citation
Publisher

Abstract

This paper introduces a quantum-inspired Qubit neural tree networks with improved Qubit neuron, overlayer connections and different phase operation functions for different neurons to construct disease classification model. A hybrid evolutionary algorithm that combines the modified PIPE algorithm with beetle antennae search is also proposed to optimize the structure and parameters of the Qubit neural tree network. Simulation results on two disease classification problems show that this model has more advantages in classification precision, feature selection and structure simplification, especially for classification with multi-class attributes.

DOI
10.1109/itme.2018.00043

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Using the properties of quantum superposition, we propose a quantum classification algorithm to efficiently perform multi-class classification tasks, where the training data are loaded into parameterized operators which are applied to the basis of the quantum state in quantum circuit composed by \emph{sample register} and \emph{label register}, and the parameters of quantum gates are optimized by a hybrid quantum-classical method, which is composed of a trainable quantum circuit and a gradient-based classical optimizer. After several quantum-to-class repetitions, the quantum state is optimal that the state in \emph{sample register} is the same as that in \emph{label register}. %A structure of loading data many times is performed as a quantum version of neural network to improve the expression ability of quantum circuit. For a classification task of LLL-class, the analysis shows that the space and time complexity of the quantum circuit are O(L∗logL)O(L*logL)O(L∗logL) and O(logL)O(logL)O(logL), respectively. The numerical simulation results of 2-class task and 5-class task show that the proposed algorithm has a higher classification accuracy, faster convergence and higher expression ability. The classification accuracy and the speed of converging can also be improved by increasing the number times of applying multi-qubit controlled operators on the quantum circuit, especially for multiple classes classification.
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