Quantum walk-based portfolio optimisation

N. Slate; E. Matwiejew; S. Marsh; J. B. Wang

doi:10.22331/q-2021-07-28-513

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

Published:	2021-07-28, volume 5, page 513
Eprint:	arXiv:2011.08057v3
Doi:	https://doi.org/10.22331/q-2021-07-28-513
Citation:	Quantum 5, 513 (2021).

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Abstract

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.

Featured image: The historic and projected expected returns for an optimised financial portfolio using the quantum walk-based optimisation algorithm.

► BibTeX data

@article{Slate2021quantumwalkbased,
  doi = {10.22331/q-2021-07-28-513},
  url = {https://doi.org/10.22331/q-2021-07-28-513},
  title = {Quantum walk-based portfolio optimisation},
  author = {Slate, N. and Matwiejew, E. and Marsh, S. and Wang, J. B.},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {5},
  pages = {513},
  month = jul,
  year = {2021}
}

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[2] T. Bennett, E. Matwiejew, S. Marsh, and J. B. Wang, "Quantum Walk-Based Vehicle Routing Optimisation", 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).

[4] Yehui Tang, Junchi Yan, Guoqiang Hu, Baohua Zhang, and Jinzan Zhou, "Recent progress and perspectives on quantum computing for finance", Service Oriented Computing and Applications 16 4, 227 (2022).

[5] Yue Ruan, Zhiqiang Yuan, Xiling Xue, and Zhihao Liu, "Quantum approximate optimization for combinatorial problems with constraints", Information Sciences 619, 98 (2023).

[6] Sebastian Brandhofer, Daniel Braun, Vanessa Dehn, Gerhard Hellstern, Matthias Hüls, Yanjun Ji, Ilia Polian, Amandeep Singh Bhatia, and Thomas Wellens, "Benchmarking the performance of portfolio optimization with QAOA", Quantum Information Processing 22 1, 25 (2022).

[7] Sanjeet Swaroop Panda, P. A. Ameen Yasir, and C. M. Chandrashekar, "Quantum direct communication protocol using recurrence in k -cycle quantum walks", Physical Review A 107 2, 022611 (2023).

[8] Tavis Bennett, Edric Matwiejew, Sam Marsh, and Jingbo B. Wang, "Quantum walk-based vehicle routing optimisation", arXiv:2109.14907, (2021).

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