← Back to papers
Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization
'Alvaro Nodar, Irene De Le'on, Danel Arias, Ernesto Mamedaliev, M. Molina, Manuel Mart'in-Cordero, Senaida Hern'andez-Santana, Pablo Serrano, M. Arranz, Oier Mentxaka, Valent'in Garc'ia, Ginés Carrascal, A. Retolaza, Inmaculada Posadillo·December 26, 2024
Physics
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
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.