量子神经网络是量子计算和人工神经网络的结合体，相关研究结果表明它可能成为未来信息处理的重要手段。人工神经树网络模型则是一种通过树编码进化算法和启发式搜索算法实现网络结构和参数优化的神经网络模型，具有网络结构自适应、参数优化简单等特点。本课题以量子神经网络中研究和应用较多的量子Qubit神经网络为切入点，通过改进量子Qubit神经元，将其引入神经树网络模型，提出一种新的量子Qubit神经树网络模型，并对模型的构建、优化和实验仿真等问题进行研究。课题预期研究目标包括三部分：(1) 改进量子Qubit神经元，给出量子Qubit神经树网络模型的构建和描述方法；(2)提出三种改进的基于树编码的进化算法，实现所提模型的结构优化； (3) 研究并搭建所提模型的仿真实验平台，对相关研究内容进行验证和分析。

Quantum neural network is a combination with quantum computation, related research results show that it may become an important way for information process in future. Artificial neural tree network model is a neural network model, of which structure and parameters are obtained by tree-coding evolutionary algorithms and heuristic search algorithms, respectively and with characteristics of adaptive network structure, simple parameters optimization and so on. The most studied quantum Qubit neural network in quantum neural networks is selected as the breakpoint of this project, by improving the quantum Qubit neuron and introduced into neural tree network model, a new quantum Qubit neural tree network model(QQNTNM) is proposed, meanwhile the construction, description, optimization and simulation problems in the model are studied. Related works are expected to complete the following three research goals: (1) Improve the quantum Qubit neuron, introduce it into neural tree network model and give the construction and description method of the QQNTNM; (2) Propose three improved tree-based evolution algorithms for optimizing the structure of the QQNTNM; (3) Build the simulation experiment platform for the QQNTNM to make validation and analysis on the related works.