Quantum Computing in Cybersecurity

15Quantum Computing to the Advantage of Neural Network

Aditya Maltare*, Ishita Jain, Keshav Agrawal and Tanya Rawat

Department of Computer Science Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India

Abstract

Artificial neural networks (ANN) have been shown to be effective in various machine learning–based big data analytics challenges. An ANN can learn and generalize the intricate and irregular aspects of the input data. In the era of big data, colossal amounts of data come from various sources. It is anticipated that a point will be reached where even supercomputers will likely be overwhelmed by the enormous data. Due to the volume and scope of the huge data, training an ANN in this situation is a difficult process.

To identify patterns and evaluate the data, a vast number of parameters must be employed and tuned in the network. Since a quantum computer may represent data in various ways utilizing qubits, quantum computing (QC) is emerging as a subject that offers a solution to this issue. It is possible to identify hidden patterns in data that are challenging for a classical computer to find by using qubits on quantum computers. As a result, the field of ANN has a wide range of potential applications. In this work, our main goal was to train an artificial neural network with qubits acting as its synthetic neurons.

The simulation results demonstrate that, when compared to conventional ANN, our QC solution for ANN (QC ANN) is effective. For a binary classification ...