Shahriar, ShadmanMajumder, Arnob2025-01-162025-01-16©20242024-10ID 24141075http://hdl.handle.net/10361/25190Cataloged from PDF version of thesis.Includes bibliographical references (pages 29-30).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.Quantum computing is a new type of computing system that is rapidly emerging with immense success in the area of computer science. In our day-to-day lives, there are different types of sounds in our surroundings, which provide us with a lot of information and data. We need to extract the noise and collect important information from it. Convolutional neural networks (CNN) and other techniques have been used for audio classification tasks for several years with high accuracy. But, quantum computing has never been used for audio classifications. So, our goal in this work is to investigate the potential of quantum advantage by experimenting with certain quantum techniques for this specific task. We will scrutinize the effectiveness of the hybrid Quantum Convolutional Neural Network. Also, we check whether it is capable of classifying or optimizing the classification task or not in its Noisy Intermediate Scale-Quantum (NISQ) era.39 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Quantum computingConvolutional neural networkAudio classificationQuantum CNNNeural networks (Computer science).Reversible computing.Quantum theory.Signal processing--Digital techniques.Computer sound processing.Speech processing systems.Thesis