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An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization

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Abstract

Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains.

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Acknowledgements

The first author was supported by the Programme New Talents in Quantum Technologies of the Gulbenkian Foundation (Portugal). This work was partially funded by national funds through the Fundação para a Ciência e a Tecnologia within the scope of the project CISUC – UID/CEC/00326/2020; by Instituto de Telecomunicações and Fundação para a Ciência e a Tecnologia under grant UIDB/50008/2020; and by the Artificial Intelligence and Computer Science Laboratory, University of Porto (LIACC), FCT/UID/CEC/0027/2020, funded by national funds through the FCT/MCTES (PIDDAC).

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Authors and Affiliations

  1. Department of Informatics Engineering, Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, s/n, Porto, 4200-465, Portugal

    Cláudio Gomes & João Paulo Fernandes

  2. Artificial Intelligence and Computer Science Laboratory (LIACC), Rua Dr. Roberto Frias, s/n, Porto, 4200-465, Portugal

    Cláudio Gomes & João Paulo Fernandes

  3. Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA

    Cláudio Gomes

  4. Faculty of Sciences and Technology of the University of Coimbra, Rua Sílvio Lima, Universidade de Coimbra – Polo II, Coimbra, 3030-790, Portugal

    Gabriel Falcao & Luís Paquete

  5. Instituto de Telecomunicações, Coimbra, Portugal

    Gabriel Falcao

  6. Centre for Informatics and Systems of the University of Coimbra, Polo II, Pinhal de Marrocos, Coimbra, 3030-290, Portugal

    Luís Paquete

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  1. Cláudio Gomes

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Correspondence toCláudio Gomes.

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