Advances of Quantum Machine Learning

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Author(s): Bhanu Chander (Pondicherry University, India)
Copyright: 2021
Pages: 19
Source title: Limitations and Future Applications of Quantum Cryptography
Source Author(s)/Editor(s): Neeraj Kumar (Babasaheb Bhimrao Ambedkar University, Lucknow, India), Alka Agrawal (Babasaheb Bhimrao Ambedkar University, Lucknow, India), Brijesh K. Chaurasia (Indian Institute of Information Technology, India)and Raees Ahmad Khan (Indian Institute of Information Technology, India)
DOI: 10.4018/978-1-7998-6677-0.ch013

Keywords: Cryptography / Cyber and Network Security / Information Science Reference / Security & Forensics

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Abstract

The basic idea of artificial intelligence and machine learning is that machines have the talent to learn from data, previous experience, and perform the work in future consequences. In the era of the digitalized world which holds big data has long-established machine learning methods consistently with requisite high-quality computational resources in numerous useful and realistic tasks. At the same time, quantum machine learning methods work exponentially faster than their counterparts by making use of quantum mechanics. Through taking advantage of quantum effects such as interference or entanglement, quantum computers can proficiently explain selected issues that are supposed to be tough for traditional machines. Quantum computing is unexpectedly related to that of kernel methods in machine learning. Hence, this chapter provides quantum computation, advance of QML techniques, QML kernel space and optimization, and future work of QML.

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