Quantum Computing in Cybersecurity
by Romil Rawat, Rajesh Kumar Chakrawarti, Sanjaya Kumar Sarangi, Jaideep Patel, Vivek Bhardwaj, Anjali Rawat, Hitesh Rawat
12Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
P. William1*, Vivek Parganiha2 and D.B. Pardeshi3
1Department of Information Technology, Sanjivani College of Engineering, SPPU, Pune, India
2Department of Computer Science Engineering, Bhilai Institute of Technology, CSVTU, Bhilai, India
3Department of Electrical Engineering, Sanjivani College of Engineering, SPPU, Pune, India
Abstract
Quantum computing has undergone a major spike in recent years. Quantum machine learning, often known as QML, is a fascinating subject of quantum computing that takes advantage of the high-dimensional Hilbert space to acquire richer representations from less input. This is done in order to effectively solve challenging learning tasks. There has not been a lot of research into QML’s safety, despite the language’s rising popularity. In this research, we analysed where QML could go in terms of hardware security in the future. Furthermore, we expose the vulnerabilities of QML and designing attack models, along with the best practises for fixing them.
Keywords: Quantum computing, hardware security, quantum neural network, attacks & defenses
12.1 Introduction
In terms of the possibilities it offers, quantum computing represents a fundamental paradigm change in the way that computers are conceived of and constructed. In spite of the fact that quantum computing is still in its infancy, the scientific community is searching for quantum computers in the hope that they ...