Quantum walk-based portfolio optimisation

N. Slate, E. Matwiejew, S. Marsh, and J. B. Wang

Department of Physics, The University of Western Australia, Perth WA 6009, Australia

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This paper proposes a highly efficient quantum algorithm for portfolio optimisation targeted at near-term noisy intermediate-scale quantum computers. Recent work by Hodson et al. (2019) explored potential application of hybrid quantum-classical algorithms to the problem of financial portfolio rebalancing. In particular, they deal with the portfolio optimisation problem using the Quantum Approximate Optimisation Algorithm and the Quantum Alternating Operator Ansatz. In this paper, we demonstrate substantially better performance using a newly developed Quantum Walk Optimisation Algorithm in finding high-quality solutions to the portfolio optimisation problem.

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Cited by

[1] Ioannis Kolotouros and Petros Wallden, "Evolving objective function for improved variational quantum optimization", Physical Review Research 4 2, 023225 (2022).

[2] T. Bennett, E. Matwiejew, S. Marsh, and J. B. Wang, "Quantum Walk-Based Vehicle Routing Optimisation", arXiv:2109.14907, Frontiers in Physics 9, 730856 (2021).

[3] Edric Matwiejew and Jingbo B. Wang, "QuOp_MPI: A framework for parallel simulation of quantum variational algorithms", Journal of Computational Science 62, 101711 (2022).

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