A Quantum Genetic Algorithm with application to Cosmological Parameters Estimation

TL;DR

This paper introduces a quantum genetic algorithm that leverages quantum circuits to optimize cosmological parameters, demonstrating its effectiveness and consistency with classical methods in estimating the universe's key properties.

Contribution

The paper presents a novel amplitude-encoded quantum genetic algorithm for cosmological parameter estimation, integrating quantum circuit operations with classical merit function evaluation.

Findings

AEQGA produces results consistent with classical optimization methods.

The algorithm's performance is stable across different hyperparameters.

AEQGA shows potential for quantum-enhanced cosmological inference.

Abstract

An Amplitude-Encoded Quantum Genetic Algorithm (AEQGA) has been developed to minimize $\chi^2$ functions of different cosmological probes (Supernovae Type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background Radiation), to find the best-fit value for two cosmological parameters, namely the Hubble Constant and the density matter content of the Universe today. Our main aim is to pave the way to testing the adoption of quantum optimization in the inference of the cosmological parameters that describe the universe evolution. AEQGA computes the merit function classically, and then uses a quantum circuit to entangle the population and perform crossover and mutation operations. The results show consistency with the isocontours of the objective functions. We then tested the general behavior of AEQGA as a function of its hyperparameters and compared it with a second quantum genetic algorithm found in the literature as well as with classical algorithms, finding consistent results.