Machine Learning Approaches to Building Quantum Circuits for Sets of Matrices

TL;DR

This paper demonstrates how interpretable machine learning can be used to construct universal, shortest analytic quantum algorithms for arbitrary diagonal matrices of any size.

Contribution

It introduces a novel approach using interpretable machine learning to design quantum algorithms, achieving universality and minimality.

Findings

Successfully constructed universal shortest analytic quantum algorithms

Applicable to arbitrary diagonal matrices of any size

Showcases the potential of interpretable machine learning in quantum algorithm design

Abstract

Machine learning nowadays becomes a useful instrument in many subjects. In this paper we use interpretable machine learning to build quantum algorithm. By studying the parameters of the machine learning algorithm we were able to construct universal shortest analytic quantum algorithm for arbitrary diagonal matrix of any size.