A quantum “black box” for entropy calculation

Abstract

A significant part of global quantum computing research has been conducted based on quantum mechanics, which can now be used with quantum computers. However, designing a quantum algorithm requires a deep understanding of quantum mechanics and physics procedures. This work presents a generic quantum “black box” for entropy calculation. It does not depend on the data type and can be applied to building and maintaining machine learning models. The method has two main advantages. First, it is accessible to those without preliminary knowledge of quantum computing. Second, it is based on the quantum circuit with a constant depth of three, which is equivalent to three operations the circuit would perform to achieve the same result. We implemented our method using the IBM simulator and tested it over different types of input. The results showed a high correspondence between the classical and quantum computations that raised an error of up to 8.8e−16 for different lengths and types of information.