Portfolio construction using a sampling-based variational quantum scheme
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908634880933888 |
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| author | Agliardi, Gabriele Alevras, Dimitris Kumar, Vaibhaw Nardo, Roberto Lo Compostella, Gabriele Kumar, Sumit Proissl, Manuel Mehta, Bimal |
| author_facet | Agliardi, Gabriele Alevras, Dimitris Kumar, Vaibhaw Nardo, Roberto Lo Compostella, Gabriele Kumar, Sumit Proissl, Manuel Mehta, Bimal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13557 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Portfolio construction using a sampling-based variational quantum scheme Agliardi, Gabriele Alevras, Dimitris Kumar, Vaibhaw Nardo, Roberto Lo Compostella, Gabriele Kumar, Sumit Proissl, Manuel Mehta, Bimal Quantum Physics Emerging Technologies Computational Finance 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. |
| title | Portfolio construction using a sampling-based variational quantum scheme |
| topic | Quantum Physics Emerging Technologies Computational Finance |
| url | https://arxiv.org/abs/2508.13557 |