A parameterized quantum circuit for estimating distribution measures

Abstract Quantum computing is a new and exciting field with the potential to solve some of the world’s most challenging problems. Currently, with the rise of quantum computers, the main challenge is the creation of quantum algorithms (under the limitations of quantum physics) and making them accessible to scientists who are not physicists. This study presents a parametrized quantum circuit and its implementation in estimating the distribution measures for discrete value vectors. Various applications can be derived from this method, including information analysis, exploratory data analysis, and machine learning algorithms. This method is unique in providing access to quantum computation and enabling users to run it without prior knowledge of quantum physics. The proposed method was implemented and tested over a dataset and five discrete value distributions with different parameters. The results showed a high level of agreement between the classical computation and the proposed method using quantum computing. The maximum error obtained for the dataset was 5.996%, while for the discrete distributions, a maximum error of 5% was obtained.