QUANTUM NEURAL NETWORKS FOR DISEASE TREATMENT IDENTIFICATION
QUANTUM NEURAL NETWORKS FOR DISEASE TREATMENT IDENTIFICATION
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用于疾病治疗识别的量子神经网络
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Vicente García Díaz
中科院分区：
文献类型：
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作者：
V. Dutt;Sriramakrishnan Chandrasekaran;Vicente García Díaz
机器学习是解决与现实世界有关的问题的先进方法。现实世界的问题可以在医学科学的帮助下得到解决，在医学科学中，可以为一个特定的问题找到大量不同的解决方案。QNN（量子神经网络）的实现是解决疾病识别问题的最佳途径。量子机器学习分为两个不同的部分。第一部分介绍了量子数据的概念，量子数据是在自然量子系统或人工系统中形成的数据。第二种方法是混合模型，它是量子科学机器学习的高级版本。QML是一种较好的疾病关系分析方法。这种疾病的症状也可以使用机器学习模型进行分析。这里应用了一种高级的QNN，这对于分析影响人的症状至关重要。已为拟议的方法确定了一系列规定的过程。此后，在使用机器学习识别疾病关系方面取得的准确性相当高。QCN（量子通信网络）在识别疾病症状和治疗关系方面的准确率约为93%。
: : Machine learning is the advanced methodology to solve the issues related to the real world. The problems of the real-world can be solved with the help of medical science, where plenty of varied solutionscan be found for a particular problem. Implementation of the QNN (Quantum Neural Networks) is the best way to solve the problem of identification of diseases. Quantum machine learning is divided into two distinct parts. The first part describes the concept of Quantum data, which is data formed in the natural quantum system or an artificial system. The second method is the Hybrid model which is an advanced version of quantum science machine learning. QML is a better way to analyse the disease relations. The symptoms of the disease can also be analysed using the machine learning model.An advanced level of QNN has been applied here, which is of utmost importance for analysing thesymptoms affecting the person. A chain of prescribed processes has been identified forthe proposed methodology. The accuracy achieved thereafter, in identifying the disease relations using machine learning, was quite high. QCN (Quantum Communication Networks)worked recorded approximately 93% accuracy in identifying the disease symptoms and treatment relations.