Performance Comparison of QAOA Mixers for Ternary Portfolio Optimization

TL;DR

This paper evaluates different QAOA mixers for ternary portfolio optimization, analyzing their performance in noiseless and noisy quantum environments using real stock data.

Contribution

It introduces a comparison of various QAOA mixers for ternary portfolio optimization, including new insights into their performance under noise conditions.

Findings

XY Mixers outperform standard mixers in noiseless environments

Performance of mixers degrades with increased noise levels

Optimal mixer choice depends on QAOA depth and noise strength

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm proposed for Noisy Intermediate-Scale Quantum (NISQ) devices and is regarded as a promising approach to combinatorial optimization problems, with potential applications in the financial sector. In this study, we apply QAOA to the portfolio optimization problem, which is one of the central challenges in financial engineering. A portfolio consists of a combination of multiple assets, and the portfolio optimization problem aims to determine the optimal asset allocation by balancing expected return and risk. In the context of quantum optimization, portfolio optimization is often formulated using discrete variables. Unlike conventional binary formulations, we consider a ternary portfolio optimization problem that accounts for three states-holding, not holding, and short selling-and compare its performance using different mixer operators. Specifically, we implement QAOA with the standard mixer and several XY Mixers (XY Ring, XY Parity Ring, XY Full, and QAMPA), and conducted simulations using real data based on the German stock index (DAX 30) for portfolios consisting of 5 and 8 assets. Furthermore, we introduce noise based on a depolarizing channel to investigate the behavior of the algorithm in realistic environments. The results show that while XY Mixers exhibit superiority in noiseless settings, their advantage degrades in noisy environments, and the optimal choice of mixer depends on both the number of QAOA depths and the noise strength.