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Quantum Optimization

Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization

arXiv
Authors: Álvaro Nodar, Irene De León, Danel Arias, Ernesto Mamedaliev, María Esperanza Molina, Manuel Martín-Cordero, Senaida Hernández-Santana, Pablo Serrano, Miguel Arranz, Oier Mentxaka, Valentín García, Ginés Carrascal, Ander Retolaza, Inmaculada Posadillo

Year

2024

Paper ID

56960

Status

Preprint

Abstract Read

~2 min

Abstract Words

81

Citations

N/A

Abstract

This work explores the potential of the Variational Quantum Eigensolver in solving Dynamic Portfolio Optimization problems surpassing the 100 qubit utility frontier. We systematically analyze how to scale this strategy in complexity and size, from 6 to 112 qubits, by testing different combinations of ansatz and optimizer on a real Quantum Processing Unit. We achieve best results by using a combination of a Differential Evolution classical optimizer and an ansatz circuit tailored to both the problem and the properties of the Quantum Processing Unit.

Why This Paper Matters

  • This paper contributes to the Quantum Optimization research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • This work explores the potential of the Variational Quantum Eigensolver in solving Dynamic Portfolio Optimization problems surpassing the 100 qubit utility frontier.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2412.19150 [2] arXiv https://arxiv.org/abs/2412.19150 [3] Publisher https://arxiv.org/abs/2412.19150

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