Portfolio construction using a sampling-based variational quantum scheme

TL;DR

This paper explores a sampling-based variational quantum algorithm for portfolio construction, demonstrating its potential to outperform classical methods in complex, large-scale financial optimization problems using IBM quantum processors.

Contribution

It introduces a novel quantum-classical workflow for portfolio optimization and evaluates its performance on real quantum hardware, highlighting advantages over classical approaches.

Findings

Quantum approach achieves 0.49% relative error

Quantum circuits outperform classical local search in accuracy

Hard-to-simulate circuits may improve convergence

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.