Performance analysis of financial time-series data using quantum computing in cloud

Abstract

Quantum Computing is an emerging technology that will allow humans to revolutionise a number of industries, including finance. The prospective advantages of quantum computing over classical computing urge professionals in the field of finance to tackle a fundamental problem in finance- Portfolio Optimisation- where the objective is to allocate assets to minimize risk and maximize returns. In this research paper, we investigate the performance of quantum computing for portfolio optimization in finance using cloud computing. Traditional methods in optimisation are classical approaches to the problem. Some relevant examples from this paper are the Markowitz's Mean-Variance Optimization Model, Capital Asset Pricing Model, VAR and CVAR. However, these methods have limitations, such as the curse of dimensionality, that make them computationally intensive for large portfolios. Quantum computing provides a potential solution to overcome these limitations. In this research, we compare the performance of quantum optimization methods with traditional portfolio optimization methods using a cloud quantum computing platform provided by IBM. We conduct experiments on real-world financial time series data of a sample portfolio by implementing the quantum algorithm for portfolio optimization proposed by H. Qian et al. in 2017, which uses the quantum amplitude estimation algorithm to estimate the optimal portfolio weights in a binary setting. Our results bolster the claim of a computational speedup in quantum techniques. The quantum algorithm is able to handle large portfolios and high dimensional datasets with significantly less computational resources than classical algorithms. Moreover, the quantum algorithm provides more options of diverse and efficient portfolios, allowing the user to make a comprehensive analysis on portfolio diversification options. Our study adds to the expanding body of literature on financial applications of quantum computing. Our results suggest that quantum computing can significantly improve portfolio optimization in finance, which has important implications for asset management and investment strategies, and how cloud computing offers a scalable and affordable alternative for non-institutional users.