Research Article
Quantum Computing Simulated Annealing Algorithm Applying in Portfolio Optimization Problem
@INPROCEEDINGS{10.4108/eai.1-9-2023.2338767, author={Baoyuan Shan and Zucheng Shang}, title={Quantum Computing Simulated Annealing Algorithm Applying in Portfolio Optimization Problem}, proceedings={Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, September 1--3, 2023, Chongqing, China}, publisher={EAI}, proceedings_a={ICPDI}, year={2023}, month={11}, keywords={quantum annealing; quadratic unconstrained binary optimization; portfolio optimization; modern portfolio theory}, doi={10.4108/eai.1-9-2023.2338767} }- Baoyuan Shan
Zucheng Shang
Year: 2023
Quantum Computing Simulated Annealing Algorithm Applying in Portfolio Optimization Problem
ICPDI
EAI
DOI: 10.4108/eai.1-9-2023.2338767
Abstract
The quantum annealing algorithm is an optimization algorithm which utilizes the quantum tunnelling effect produced by quantum fluctuations to escape local optima, thus enhancing the chance of finding the global optimal solution. Portfolio optimization problems effectively describe activities such as ordinary stock investments and asset management as optimization problems in portfolio investments. The quantum annealing algorithm leverages the unique properties of quantum mechanics, which could significantly improve the efficiency and effectiveness of solutions. This paper proposes a modelling and solution approach for the portfolio investment problem based on a quantum computing framework. The method utilizes the quantum annealing algorithm to optimize the real stock price trends in financial markets, building on the foundation of modern portfolio theory.
