Exploring the synergistic potential of quantum annealing and gate model computing for portfolio optimization

TL;DR

This paper proposes a hybrid quantum approach combining annealing and gate-based computing to efficiently solve large-scale portfolio optimization problems, demonstrating comparable performance to classical methods on real stock data.

Contribution

It introduces a novel modification to the Large System Sampling Approximation (LSSA) method, enhancing its effectiveness for large portfolio problems using hybrid quantum computing.

Findings

Hybrid approach performs on par with classical methods

Effective on real-world Indian stock data with up to 64 assets

Parameter analysis shows robustness across different problem sizes

Abstract

Portfolio optimization is one of the most studied problems for demonstrating the near-term applications of quantum computing. However, large-scale problems cannot be solved on today's quantum hardware. In this work, we extend upon a study to use the best of both quantum annealing and gate-based quantum computing systems to enable solving large-scale optimization problems efficiently on the available hardware. The existing work uses a method called Large System Sampling Approximation (LSSA) that involves dividing the large problem into several smaller problems and then combining the multiple solutions to approximate the solution to the original problem. This paper introduces a novel technique to modify the sampling step of LSSA. We divide the portfolio optimization problem into sub-systems of smaller sizes by selecting a diverse set of assets that act as representatives of the entire market and capture the highest correlations among assets. We conduct tests on real-world stock data from the Indian stock market on up to 64 assets. Our experimentation shows that the hybrid approach performs at par with the traditional classical optimization methods with a good approximation ratio. We also demonstrate the effectiveness of our approach on a range of portfolio optimization problems of different sizes. We present the effects of different parameters on the proposed method and compare its performance with the earlier work. Our findings suggest that hybrid annealer-gate quantum computing can be a valuable tool for portfolio managers seeking to optimize their investment portfolios in the near future.