Benchmarking the performance of portfolio optimization with QAOA

We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the...

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Vydáno v:Quantum information processing Ročník 22; číslo 1
Hlavní autoři: Brandhofer, Sebastian, Braun, Daniel, Dehn, Vanessa, Hellstern, Gerhard, Hüls, Matthias, Ji, Yanjun, Polian, Ilia, Bhatia, Amandeep Singh, Wellens, Thomas
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 16.12.2022
Témata:
Data Structures and Information Theory
Mathematical Physics
Physics
Physics and Astronomy
Quantum Computing
Quantum Information Technology
Quantum Physics
Spintronics
Quantum computing
Quantum algorithm
Quantum optimization
QAOA
ISSN:1573-1332, 1573-1332
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Shrnutí:We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the portfolio. QAOA has been suggested as a possible candidate for solving this problem (and similar combinatorial optimization problems) more efficiently than classical computers in the case of a sufficiently large number of assets. However, the practical implementation of this algorithm requires a careful consideration of several technical issues, not all of which are discussed in the present literature. The present article intends to fill this gap and thereby provides the reader with a useful guide for applying QAOA to the portfolio optimization problem (and similar problems). In particular, we will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. Viewing at the application of QAOA on error-prone NISQ hardware, we also analyse the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware). Finally, we define a criterion for distinguishing between ‘easy’ and ‘hard’ instances of the portfolio optimization problem.
ISSN:1573-1332
1573-1332
DOI:10.1007/s11128-022-03766-5