Hybrid quantum-classical optimization for financial index tracking
This work addresses index tracking for financial analysts, but it appears incremental as it builds on existing hybrid quantum-classical methods without demonstrating clear superiority.
The paper tackled the NP-hard problem of financial index tracking by introducing a heuristic pruning algorithm to select weighted asset combinations under cardinality constraints, and compared quantum ansätze and classical optimizers through simulations, though no concrete performance numbers were provided.
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 ansätze and classical optimizers through numerical simulations.