Hybrid quantum-classical optimization for financial index tracking

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 ansätze and classical optimizers through numerical simulations.

24 pages, 12 figures. A few changes in structure implemented in version 2

References in corpus (5)

• A Quantum Approximate Optimization Algorithm
• A direct formulation for sparse PCA using semidefinite programming
• Training a Binary Classifier with the Quantum Adiabatic Algorithm
• Hybrid quantum-classical optimization for financial index tracking
• Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz

Cited by in corpus (7)

• Quantum computing for finance
• Filtering variational quantum algorithms for combinatorial optimization
• Quantum variational optimization: The role of entanglement and problem hardness
• Hybrid quantum-classical optimization for financial index tracking
• Approaches to Constrained Quantum Approximate Optimization
• A systematic literature review on solution approaches for the index tracking problem in the last decade
• Demonstration of Hardware Efficient Photonic Variational Quantum Algorithm