A METHODOLOGY FOR ENCODER USING DATA COMPRESSION AND OBFUSCATION TECHNIQUES IN QUANTUM COMPUTING

Abstract

- Classical data are often compressed by using simple procedure which allows a string of data to take up less space during a computer’s memory. But, In Quantum Computing Data Compression is different because Quantum data are different and it's impossible to decide the frequencies of 1’s and 0’s in quantum information. The structure of the underlying autoencoder network is often chosen to represent the information on a smaller dimension, effectively compressing the input. Inspired by this concept, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a specific dataset of quantum states, where a classical compression algorithm cannot be employed. Using classical optimization algorithms, the parameters of the quantum autoencoder are trained. We show an example of an easy programmable circuit which will be trained as an efficient autoencoder with and without using dummy swap gates are implemented in IBMQ.