Quantum Computing in Cybersecurity

6An Overview of Quantum Computing–Based Hidden Markov Models

B. Abhishek¹, Sathian D.¹, Amit Kumar Tyagi²* and Deepshikha Agarwal³

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

²Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, Delhi, India

³Department of Information Technology, IIIT Lucknow, Uttar Pradesh, India

Abstract

The basic concept of Markov chains has been known to mathematicians and engineers for the last 80 years, but it was only in the last decade that it was directly applied to speech processing difficulties. One of the significant reasons why speech models based on Markov chains had not been created until recently was the lack of a strategy for modifying the parameters of the Markov model to suit actual signal patterns. Further advancements in theory and execution have resulted in a diverse range of applications for Markov modeling approaches. The goal of this study is to provide an introduction to Markov model theory based on quantum computing and demonstrate how it has been used to voice recognition issues.

Keywords: Markov models, learning algorithms, speech processing, voice recognition

6.1 Introduction

Suppose you’ve been given the following situation. A sequence of observable symbols [1–3] is produced by a real-world process. The symbols might be discrete or continuous.

Certain fundamental decisions must be taken to tackle such a challenge, informed by signal ...