Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks

@article{Mugel2022DynamicPO,
  title={Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks},
  author={Samuel Mugel and Carlos Kuchkovsky and Escolastico Sanchez and Samuel Fern{\'a}ndez-Lorenzo and Jorge Luis-Hita and Enrique Lizaso and Rom{\'a}n Or{\'u}s},
  journal={ArXiv},
  year={2022},
  volume={abs/2007.00017}
}
In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem, well-known to be NP-Hard, is central to quantitative finance. After a detailed introduction to the problem, we implement a number of quantum and quantum-inspired algorithms on different hardware platforms to solve its discrete… 

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