This work explores the potential of the Variational Quantum Eigensolver in solving Dynamic Portfolio Optimization problems surpassing the 100 qubit utility frontier. We systematically analyze how to scale this strategy in complexity and size, from 6 to 112 qubits, by testing different combinations of ansatz and optimizer on a real Quantum Processing Unit. We achieve best results by using a combination of a Differential Evolution classical optimizer and an ansatz circuit tailored to both the problem and the properties of the Quantum Processing Unit.
@article{arxiv.2412.19150,
title = {Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization},
author = {Álvaro Nodar and Irene De León and Danel Arias and Ernesto Mamedaliev and María Esperanza Molina and Manuel Martín-Cordero and Senaida Hernández-Santana and Pablo Serrano and Miguel Arranz and Oier Mentxaka and Valentín García and Ginés Carrascal and Ander Retolaza and Inmaculada Posadillo},
journal= {arXiv preprint arXiv:2412.19150},
year = {2025}
}