Authors - Rani Nandkishor Aher, Nandkishor Daulat Aher Abstract - Data compression is a common feature of current information processing and has a broad range of uses. Audio compression is critical in database applications such as storage and transmission. The difficulty of lowering the number of bits required to represent digital audio is addressed via audio compression. It is used to reduce redundancy by reducing irrelevant, redundant data. Recent developments in deep learning have motivated researchers to investigate challenges involving highly structured data utilizing unified deep network models. Due to the necessity for discrete representations that are difficult to train, the construction and design of such models for compressing audio signals have proven difficult. The purpose of this study is to concentrate on quantum neural networks for compressing audio files. As a result, a quantum based auto encoder capable of compressing audio data into a low-dimensional space is critical for achieving automatic audio compression and in the decoder part decompress the quantum audio signals by using deconvolution layers. Finally, the quantum representation of audio signals retrieves the original signals.
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