Portfolio optimization (PO) is extensively employed in financial services to assist in achieving investment objectives. By providing an optimal asset allocation, PO effectively balances the risk and returns associated with investments. However, it is important to note that as the number of involved assets and constraints increases, the portfolio optimization problem can become increasingly difficult to solve, falling into the category of NP-hard problems. In such scenarios, classical algorithms, such as the Monte Carlo method, exhibit limitations in addressing this challenge when the number of stocks in the portfolio grows. Quantum annealing algorithm holds promise for solving complex portfolio optimization problems in the NISQ era. Many studies have demonstrated the advantages of various quantum annealing algorithm variations over the standard quantum annealing approach. In this work, we conduct a numerical investigation of randomly generated unconstrained single-period discrete mean-variance portfolio optimization instances. We explore the application of a variety of unconventional quantum annealing algorithms, employing both forward annealing and reverse annealing schedules. By comparing the time-to-solution(TTS) and success probabilities of diverse approaches, we show that certain methods exhibit advantages in enhancing the success probability when utilizing conventional forward annealing schedules. Furthermore, we find that the implementation of reverse annealing schedules can significantly improve the performance of select unconventional quantum annealing algorithms.Portfolio optimization (PO) is extensively employed in financial services to assist in achieving investment objectives. By providing an optimal asset allocation, PO effectively balances the risk and returns associated with investments. However, it is important to note that as the number of involved assets and constraints increases, the portfolio optimization problem can become increasingly difficult to solve, falling into the category of NP-hard problems. In such scenarios, classical algorithms, such as the Monte Carlo method, exhibit limitations in addressing this challenge when the number of stocks in the portfolio grows. Quantum annealing algorithm holds promise for solving complex portfolio optimization problems in the NISQ era. Many studies have demonstrated the advantages of various quantum annealing algorithm variations over the standard quantum annealing approach. In this work, we conduct a numerical investigation of randomly generated unconstrained single-period discrete mean-variance portfolio optimization instances. We explore the application of a variety of unconventional quantum annealing algorithms, employing both forward annealing and reverse annealing schedules. By comparing the time-to-solution(TTS) and success probabilities of diverse approaches, we show that certain methods exhibit advantages in enhancing the success probability when utilizing conventional forward annealing schedules. Furthermore, we find that the implementation of reverse annealing schedules can significantly improve the performance of select unconventional quantum annealing algorithms.

arXiv:2403.02599Mar 5, 20240 citations

Portfolio Risk Management Using Quantum Annealing

Thomas Doutre, Emmanuel De Meric De Bellefon

World Academy of Science, Engineering and Technology, International Journal of Mathematical and Computational SciencesSep 26, 20160 citations

Benchmarking Quantum Annealing Controls with Portfolio Optimization

Erica Grant, Travis S. Humble, Benjamin Stump

Quantum annealing offers an approach to finding the optimal solutions for a variety of computational problems, where the quantum annealing controls influence the observed performance and error mechanisms by tuning the underlying quantum dynamics. However, the influence of the available controls is often poorly understood, and methods for evaluating the effects of these controls are necessary to tune quantum computational performance. Here we use portfolio optimization as a case study by which to benchmark quantum annealing controls and their relative effects on computational accuracy. We compare empirical results from the D-Wave $2000{\mathrm{Q}}^{\mathrm{TM}}$ quantum annealer to the computational ground truth for a variety of portfolio optimization instances. We evaluate both forward and reverse annealing methods and we identify control variations that yield optimal performance in terms of probability of success and probability of chain breaks.Quantum annealing offers an approach to finding the optimal solutions for a variety of computational problems, where the quantum annealing controls influence the observed performance and error mechanisms by tuning the underlying quantum dynamics. However, the influence of the available controls is often poorly understood, and methods for evaluating the effects of these controls are necessary to tune quantum computational performance. Here we use portfolio optimization as a case study by which to benchmark quantum annealing controls and their relative effects on computational accuracy. We compare empirical results from the D-Wave $2000{\mathrm{Q}}^{\mathrm{TM}}$ quantum annealer to the computational ground truth for a variety of portfolio optimization instances. We evaluate both forward and reverse annealing methods and we identify control variations that yield optimal performance in terms of probability of success and probability of chain breaks.

arXiv:2007.03005Physical Review AppliedJan 8, 202190 citations

Portfolio Optimization: Applications in Quantum Computing

Michael Marzec

Jan 1, 201310 citations

2000{\mathrm{Q}}^{\mathrm{TM}}

quantum annealer to the computational ground truth for a variety of portfolio optimization instances. We evaluate both forward and reverse annealing methods and we identify control variations that yield optimal performance in terms of probability of success and probability of chain breaks.