Best practices for portfolio optimization by quantum computing, experimented on real quantum devices.

ABSTRACT: In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic probl

SUBMITTER: Buonaiuto G 

PROVIDER: S-EPMC10632408 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature