Quantum Portfolio Optimization with Expert Analysis Evaluation

TL;DR

This paper benchmarks quantum algorithms for portfolio optimization, revealing that while they minimize costs effectively, portfolios often lack practical financial criteria, and introduces an expert evaluation framework to address this gap.

Contribution

It systematically compares VQE and QAOA in finance, highlighting the need for expert judgment to ensure practical viability of quantum-optimized portfolios.

Findings

Both algorithms effectively minimize cost functions.

Portfolios often violate diversification and risk criteria.

Expert evaluation reveals gaps between algorithmic results and financial practicality.

Abstract

Quantum algorithms have gained increasing attention for addressing complex combinatorial problems in finance, notably portfolio optimization. This study systematically benchmarks two prominent variational quantum approaches, Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), under diverse experimental settings, including different asset universes, ansatz architectures, and circuit depths. Although both methods demonstrate effective cost function minimization, the resulting portfolios often violate essential financial criteria, such as adequate diversification and realistic risk exposure. To bridge the gap between computational optimization and practical viability, we introduce an Expert Analysis Evaluation framework in which financial professionals assess the economic soundness and the market feasibility of quantum-optimized portfolios. Our results highlight a critical disparity between algorithmic performance and financial applicability, emphasizing the necessity of incorporating expert judgment into quantum-assisted decision-making pipelines.