Achieving High-Quality Portfolio Optimization with the Variational Quantum Eigensolver

TL;DR

This paper explores using the Variational Quantum Eigensolver with a novel cost function and optimizer to improve portfolio optimization, demonstrating promising results through classical simulations.

Contribution

It introduces a new approach combining WCVaR and CMA-ES with VQE for enhanced quantum portfolio optimization performance.

Findings

WCVaR improves solution quality in VQE-based portfolio optimization.

CMA-ES effectively optimizes the VQE parameters.

Classical simulations show promising results for the proposed method.

Abstract

Portfolio optimization is a fundamental problem in finance that aims to determine the optimal allocation of assets within a portfolio to maximize returns while minimizing risk. It can be formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is NP-hard. Quantum computing offers the potential to solve such problems more efficiently than classical methods. In this work, we employ the Variational Quantum Eigensolver (VQE) to address the portfolio optimization problem. To increase the likelihood of converging to high-quality solutions, we propose using the Weighted Conditional Value-at-Risk (WCVaR) as the cost function and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) as the optimizer. Our experiments are conducted using the classical simulations on the Wuyue QuantumAI platform. The results demonstrate that the combination of WCVaR and CMA-ES leads to improved performance in solving the portfolio optimization problem.