آموزش شبکه عصبی مصنوعی
Abstract
۱٫ Introduction
۲٫ Quantum computing
۳٫ Quantum computing approach for artificial neural networks (QC ANN)
۴٫ Experimental evaluation
۵٫ Conclusion and Future Scope
References
Abstract
Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.
Introduction
Data processing is an area of concern, due to the rapid speeds at which the volume of data is growing. Getting some insights from very large amounts of data is a big challenge. IDC (International Data Corporation) in its white paper Data Age 2025 [15] predicts that, worldwide data will grow from 33ZB (1ZB = 1021bytes) in 2018 to 175ZB by 2025. Artificial neural networks [16, 12, 24] removed a lot of obstacles in big data processing, thereby, turning the data to information for successful decision making. Data growth and size create new problems to be addressed and challenge the existing computing power of artificial neural networks. An ANN can learn complex relationships from the input data using the parameters between the layers of the network. These parameters are optimized using backpropagation during the training phase. As the size and dimension of the input data grow, training a neural network with fewer parameters is a difficult task. Complex machine learning problems can be solved efficiently using a quantum computer [5]. Biamonte et.al [3] described the advantages of using quantum computing in solving machine learning problems. In their work [18, 19], Schuld et.al designed a quantum circuit as a classifier for binary classification using distance-based kernel function. Recent work [8, 10] states that machine learning problems can be solved efficiently using a hybrid classical-quantum approach that uses a classical computer to optimize quantum parameters. Tacchino et.al [22] implemented an artificial neuron on a quantum processor. Open-source software [6] are developed to implement quantum algorithms on a real quantum processor or a quantum simulator. Classical computing can store and process the data in the form of binary digits. In contrast, quantum computers store and process the data using qubits [13] which use both 0 and 1 at the same time.