Applications of Quantum Computation in Artificial Neural Network: Opportunities and Challenges

Avinash Chalumuri, Raghavendra Kune, B. S. B. S. Manoj

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.

Nasrullah Masud, Abdus Sobhan, Md Tanvir Islam Sourav +1 more

Rajesh Kannan

Hybrid Quantum-Classical Neural Networks (HQCNNs) represent a novel and promising approach that integrates the strengths of classical and quantum computing to advance artificial intelligence (AI). This paper provides a comprehensive overview of HQCNNs, including their theoretical foundations, practical implementations, and potential applications. We delve into the fundamental concepts of quantum computing, classical neural networks, and the hybrid model. The paper also explores the challenges and opportunities associated with HQCNNs, and discusses recent advancements in the field. Finally, we present a detailed case study and a novel algorithm to illustrate the practical implementation of HQCNNs. The findings suggest that HQCNNs have the potential to revolutionize various domains, from drug discovery to financial modeling, by leveraging the unique capabilities of quantum computing

Sonia Maria D’Souza, R. Ramesh Prasad, Chethan S. Reddy +3 more

M. Vasuki, A. Karunamurthy, R. Ramakrishnan +1 more

In recent years, quantum computing has emerged as a potentially gamechanging technology, with applications across various disciplines, including AI and machine learning.In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs).This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks.This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks.Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs.Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.

Huaixin Cao, Feilong Cao, Dianhui Wang