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

We tackle the problem of dynamic portfolio optimization with discrete variables.

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We tackle the problem of dynamic portfolio optimization with discrete variables. This problem, well-known to be NP-Hard, is central to quantitative finance. We find the optimal trading trajectory over 8 years for 52 assets using quantum and quantum-inspired algorithms. These are benchmarked against the current best available solvers.

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