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Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization

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arxiv 2412.19150 v2 pith:HRQGPUTX submitted 2024-12-26 quant-ph

Álvaro Nodar , Irene De León , Danel Arias , Ernesto Mamedaliev , María Esperanza Molina , Manuel Martín-Cordero , Senaida Hernández-Santana , Pablo Serrano
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Miguel Arranz Oier Mentxaka Valentín García Ginés Carrascal Ander Retolaza Inmaculada Posadillo
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classification quant-ph
keywords quantumansatzdynamiceigensolveroptimizationoptimizerportfolioprocessing
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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.

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  1. Towards secondary structure prediction of longer mRNA sequences using a quantum-centric optimization scheme

    quant-ph 2025-05 conditional novelty 5.0 of 10

    Hybrid CVaR and IQP quantum workflows find CPLEX-verified optimal solutions for mRNA-folding QUBO instances up to 156 qubits, but simulated scaling shows steeply declining success rates.

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