Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization
\'Alvaro Nodar, Irene De Le\'on, Danel Arias, Ernesto Mamedaliev,, Mar\'ia Esperanza Molina, Manuel Mart\'in-Cordero, Senaida, Hern\'andez-Santana, Pablo Serrano, Miguel Arranz, Oier Mentxaka, Valent\'in, Garc\'ia, Gin\'es Carrascal, Ander Retolaza, and Inmaculada Posadillo
TL;DR
This paper demonstrates how to scale the Variational Quantum Eigensolver for dynamic portfolio optimization from 6 to 112 qubits, optimizing performance through tailored ansatz and optimizer choices on real quantum hardware.
Contribution
It introduces a scalable approach for applying VQE to large qubit systems in portfolio optimization, surpassing previous utility limits.
Findings
Successful scaling of VQE from 6 to 112 qubits
Differential Evolution optimizer outperforms others
Tailored ansatz improves problem-specific performance
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Stochastic processes and financial applications