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

2025-12-26

AI Summary

This work extends the Variational Quantum Eigensolver (VQE) to large-scale dynamic portfolio optimization (DPO), tackling an industrial-grade instance comprising 38 assets—covering the IBEX 35 index—in a real-world financial setting. Method: To address quantum hardware limitations under QUBO formulation, we propose two key innovations: (1) Ising Sample Quantum Configuration Recovery (ISQR), which significantly improves solution quality from quantum sampling; and (2) a VQE-based constraint decomposition framework that overcomes qubit-count bottlenecks, enabling optimization beyond native hardware capacity. We integrate a hardware-aware VQE workflow and deploy it on the IBM Fez quantum processor. Contribution/Results: Experiments demonstrate competitive financial performance relative to classical solvers, while generating superior and more diverse portfolio strategies. This validates the feasibility and practical advantage of quantum optimization in realistic financial applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Extends VQE to large dynamic portfolio optimization problems.
• Handles more variables than qubits via decomposition methods.
• Achieves financial performance comparable to classical methods.

Innovation

• Scalable hardware-aware VQE methodology for finance
• ISQR routine improves solution quality in optimization
• VQE Constrained method handles large variable instances