Audio Compression Using Qubits and Quantum Neural Network
Rani Nandkishor Aher, Nandkishor Daulat Aher · Procedia Computer Science · 2024
Developing systems capable of compressing audio files is a very appealing research topic. This is due to the necessity to improve storage utilisation and accelerate data transfer over restricted communication channels. As a result, numerous investigators have explored and developed many methods to compress audio information with several approaches; everyone has negative consequences, such as excessive time consumption or complex computations, and remains a crucial problem. Recent improvements in deep learning have prompted researchers to use unified deep network models to investigate challenges needing highly organised data. The building and design of such models for compressing audio signals have been problematic due to the demand for discrete representations that are difficult to train. This research focuses on quantum neural networks for audio file compression. Our method uses an innovative encoding process that embeds audio signals in quantum states. Our framework can compress larger files than previously possible for the BBC sound dataset while achieving a better compression ratio than classical neural networks.