2025/08/19 by Gabriele Agliardi, Dimitris Alevras, Agliardi, Gabriele +12 · 1 citation
Computer Science · Economics, Econometrics and Finance · #Computational Finance (q-fin.CP) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Economics and business #FOS: Physical sciences #Financial Markets and Investment Strategies #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2508.13557
openalex publication_date 2025/08/19 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28
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.