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We present a quantum algorithm for portfolio optimisation. Specifically, We present an end-to-end quantum approximate optimisation algorithm (QAOA) to solve the discrete global minimum variance portfolio (DGMVP) model. This model finds a portfolio of
Externí odkaz:
http://arxiv.org/abs/2410.16265
This paper proposes a quasi-binary encoding based algorithm for solving a specific quadratic optimization models with discrete variables, in the quantum approximate optimization algorithm (QAOA) framework. The quadratic optimization model has three c
Externí odkaz:
http://arxiv.org/abs/2304.06915
Within the quantum computing, there are two ways to encode a normalized vector $\{ \alpha_i \}$. They are one-hot encoding and binary coding. The one-hot encoding state is denoted as $\left | \psi_O^{(N)} \right \rangle=\sum_{i=0}^{N-1} \alpha_i \lef
Externí odkaz:
http://arxiv.org/abs/2206.11166
Akademický článek
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In this paper, we propose a new quantum approximate optimization algorithm (QAOA) with binary encoding to address portfolio optimization under hard constraints. Portfolio optimization involves selecting the optimal combination of assets to achieve a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfb9bacc722cea7d2d806ef9b672b278
Publikováno v:
In Solid State Communications December 2018 284-286:25-30
