Variational Quantum Eigensolver for Real-World Finance: Scalable Solutions for Dynamic Portfolio Optimization Problems

ArXiv ID: 2512.22001 “View on arXiv”

Authors: Irene De León, Danel Arias, Manuel Martín-Cordero, María Esperanza Molina, Pablo Serrano, Senaida Hernández-Santana, Miguel Ángel Jiménez Herrera, Joana Fraxanet, Ginés Carrascal, Escolástico Sánchez, Inmaculada Posadillo, Álvaro Nodar

Abstract

We present a scalable, hardware-aware methodology for extending the Variational Quantum Eigensolver (VQE) to large, realistic Dynamic Portfolio Optimization (DPO) problems. Building on the scaling strategy from our previous work, where we tailored a VQE workflow to both the DPO formulation and the target QPU, we now put forward two significant advances. The first is the implementation of the Ising Sample-based Quantum Configuration Recovery (ISQR) routine, which improves solution quality in Quadratic Unconstrained Binary Optimization problems. The second is the use of the VQE Constrained method to decompose the optimization task, enabling us to handle DPO instances with more variables than the available qubits on current hardware. These advances, which are broadly applicable to other optimization problems, allow us to address a portfolio with a size relevant to the financial industry, consisting of up to 38 assets and covering the full Spanish stock index (IBEX 35). Our results, obtained on a real Quantum Processing Unit (IBM Fez), show that this tailored workflow achieves financial performance on par with classical methods while delivering a broader set of high-quality investment strategies, demonstrating a viable path towards obtaining practical advantage from quantum optimization in real financial applications.

Keywords: portfolio optimization, quantum computing, Variational Quantum Eigensolver, dynamic portfolio optimization, hardware-aware

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.2/10
• Quadrant: Holy Grail
• Why: The paper employs advanced quantum mechanical formalisms (VQE, QUBO, Ising Hamiltonians) and requires sophisticated algorithmic decomposition, indicating high mathematical complexity. It demonstrates concrete implementation on real quantum hardware (IBM Fez) with performance comparisons against classical benchmarks and financial metrics, showing strong empirical rigor.

  flowchart TD
    A["Research Goal: Extend VQE to Large-Scale<br>Dynamic Portfolio Optimization (DPO)"] --> B["Methodology: Scalable &<br>Hardware-Aware Workflow"]
    B --> C["Inputs: DPO Formulation<br>and IBEX 35 Market Data"]
    C --> D["Step 1: Ising Sample-based<br>Quantum Configuration Recovery"]
    D --> E["Step 2: VQE Constrained<br>Optimization Decomposition"]
    E --> F["Execution on Real QPU<br>(IBM Fez)"]
    F --> G["Outcomes:<br>Financial performance on par with classical methods<br>Broader set of high-quality strategies"]