Assignment Sample on Quantum Distributed Deep Learning Architectures
Introduction
According to Vandi Rajesh in their studies, they tried to propose a perspective towards the topic on the quantum optical convolutional neural network. The dataset of the MNIST which contains 70000 images. These images are used for the testing and training process for the models in the machine learning. This perspective method is totally based on the LeNet which is also known as CNN based. These models are also based on the concept of the ComplexNet and GridNet. These models achieve a precision of 97.4%. Matthews Correlation Coefficient (MCC) achieved a precision of 0.971 for developing the robust model in the python programming language. The most use of the rectified linear unit (Re Lu) in the Opti QUIBIDS model. This model is used for activating the function. In this model, the algorithms are very efficient as compared to other unsupervised and supervised proposed models. The developer obtained the precision level from the three datasets are of 98.5%. During the time of branching the model of the neural network algorithm in the machine learning, the essential part of the model is that it can influence the result of the measurement of the mid circuit level which is used to determine the parameters in different aspects for further usage. On further research, it is totally proved that the bQCNN model is far better than the model which is generated through CNN algorithm. To solve these problems, the developer introduced a machine learning (ML) model which is also known as quantum classical model in the deep learning method of the python programming language.
QNN model
Nowadays, the developer develops models by using QNN model which is also known as the pre trained model in python programming language or in the deep learning method for machine learning. The precision percentage of the faster distribution of the training model dataset is 98.7%. This precision percentage is for the big data or the high complexity dataset for the finance field and for the medicine field. This perspective is mainly used for the securing the data or for the data privacy and loads of computational programming for the array function NISQ computers. QCNN model can be used for both ways. It can be used for identifying the image recognition as well as for the determining the emotions of the music. Here, the researcher reviewed about the topic on the quantum distribution in the deep learning. The researcher stated the problems faced by the developers while working with the complicated and highly secured data of the medicine background and the finance background. The researcher reviewed various types of deep learning (DL) methods which will solve the problems faced by the developer.
Deep neural network (DNN)
Deep learning is one of the best technologies that has already captured the state-of-the-art for the different data security and data processing computations. It has been seen that there may occur various problems in the data computations for its large volume, and they can arise the problems on the computational overloading process. Also it has been found that these computational overloading problems are also dependent on the computational power. So to solve this problem here in this report the researcher has combined the distributive method of deep learning with the quantum distributive method. By emerging these two methods the developer ultimately had tried to complement the existing deep learning method. So moreover the process of maximization of the advantages catches the eye by applying the combination of the ‘Quantum distributed deep learning model’. So here in this part the researcher has tried to give a proper review on the ‘Quantum distributed deep learning model’ by comparing the architectural structures. Also the researcher has discussed the various problems and the possibilities of deep learning to leverage the same type of the representations of the applications scenarios. Here by researching on the other research papers the developer gets the idea of the medialization and the applications of the ‘Quantum distributed deep learning model’. This model is one of the very important ones as it has the ability to give the data security and helps in the extraction of the data, which helps in the data processing. Deep learning is the one of the parts of machine learning technology, in which by using the neural network in the context of the computational approach to mimic the human gives it the ability to resolve the high amount of complex mathematical calculations. Here in this project report the researcher has seen that deep learning has a deep impact in the field of finance and in the medical field. By increasing the trend of the sensitive data the QDDL attracts the researchers by its charms of combining the QDDL and the QDL. Moreover here the researcher has combined the protocols of the servers and the clients, to provide the high secure quantum communication. With the implementation of the blind quantum computation it also combines the QFL with the quantum federated machine learning techniques. Which helps in the efficient computation of the data privacy and in the security aspects. There are several research works and several developments are needed to make the process in a standardized look by establishing autonomous mobility and reducing the computations to make the process more lucrative and implementing the efficient communication methods and security processes to fulfill the requirements of modern technology.
Future scope
As per the studies, it is proved that quantum computing is still in the process for further practical research and also for implementing different types of models in deep learning methods for machine learning. But this theory is lacking due to the shortage of proper quantum computers. This technique of robust model will be used for the technique of the deep learning method in the near studies for machine learning. There are many startups that already implement the concept of the deep learning method and quantum computing. The best companies which are the best example for implementing the environment of the quantum computational are IBM, IQBit, etc.
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