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arXiv · 2508.13557

Portfolio construction using a sampling-based variational quantum scheme

Gabriele Agliardi·Dimitris Alevras·Vaibhaw Kumar·Roberto Lo Nardo·Gabriele Compostella·Sumit Kumar·Manuel Proissl·Bimal Mehta
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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.

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Gabriele Agliardi, Dimitris Alevras, Vaibhaw Kumar, Roberto Lo Nardo, Gabriele Compostella, Sumit Kumar, Manuel Proissl, Bimal Mehta. 2025-11-07. Portfolio construction using a sampling-based variational quantum scheme. https://arxiv.org/abs/2508.13557

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