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- W3157083359 abstract "Tracking a financial index boils down to replicating its trajectory of returns for a well-defined time span by investing in a weighted subset of the securities included in the benchmark. Picking the optimal combination of assets becomes a challenging NP-hard problem even for moderately large indices consisting of dozens or hundreds of assets, thereby requiring heuristic methods to find approximate solutions. Hybrid quantum-classical optimization with variational gate-based quantum circuits arises as a plausible method to improve performance of current schemes. In this work we introduce a heuristic pruning algorithm to find weighted combinations of assets subject to cardinality constraints. We further consider different strategies to respect such constraints and compare the performance of relevant quantum ans{a}tze and classical optimizers through numerical simulations." @default.
- W3157083359 created "2021-05-10" @default.
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- W3157083359 date "2020-01-01" @default.
- W3157083359 modified "2023-09-27" @default.
- W3157083359 title "Hybrid quantum-classical optimization for financial index tracking" @default.
- W3157083359 hasPublicationYear "2020" @default.
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