Portfolio construction using a sampling-based variational quantum scheme
Abstract
The efficient and effective construction of portfolios that adhere to real-world constraints is a challenging optimization task in finance. We investigate a concrete representation of the problem with a focus on design proposals of an Exchange Traded Fund. We evaluate the sampling-based CVaR Variational Quantum Algorithm (VQA), combined with a local-search post-processing, for solving problem instances that beyond a certain size become classically hard. We also propose a problem formulation that is suited for sampling-based VQA. Our utility-scale experiments on IBM Heron processors involve 109 qubits and up to 4200 gates, achieving a relative solution error of 0.49%. Results indicate that a combined quantum-classical workflow achieves better accuracy compared to purely classical local search, and that hard-to-simulate quantum circuits may lead to better convergence than simpler circuits. Our work paves the path to further explore portfolio construction with quantum computers.
Keywords
Cite
@article{arxiv.2508.13557,
title = {Portfolio construction using a sampling-based variational quantum scheme},
author = {Gabriele Agliardi and Dimitris Alevras and Vaibhaw Kumar and Roberto Lo Nardo and Gabriele Compostella and Sumit Kumar and Manuel Proissl and Bimal Mehta},
journal= {arXiv preprint arXiv:2508.13557},
year = {2025}
}