Benchmarking the performance of portfolio optimization with QAOA
- URL: http://arxiv.org/abs/2207.10555v1
- Date: Thu, 21 Jul 2022 15:53:46 GMT
- Title: Benchmarking the performance of portfolio optimization with QAOA
- Authors: Sebastian Brandhofer, Daniel Braun, Vanessa Dehn, Gerhard Hellstern,
Matthias H\"uls, Yanjun Ji, Ilia Polian, Amandeep Singh Bhatia, and Thomas
Wellens
- Abstract summary: We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA)
We will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters.
We also analyze the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware)
- Score: 0.12110562441696866
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 provide 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
analyze 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
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