Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions

TL;DR

This paper establishes a benchmark for large-scale dynamic portfolio optimization under market frictions, enabling fair comparison of quantum and classical computing methods to verify genuine advancements in financial strategies.

Contribution

It introduces a QUBO-based benchmark problem for portfolio optimization, facilitating objective evaluation of quantum versus classical computational approaches.

Findings

Provides a reliable benchmark for quantum and classical methods

Enables verification of true improvements in trading strategies

Uses real data to test latest solvers

Abstract

Quantum computing is poised to transform the financial industry, yet its advantages over traditional methods have not been evidenced. As this technology rapidly evolves, benchmarking is essential to fairly evaluate and compare different computational strategies. This study presents a challenging yet solvable problem of large-scale dynamic portfolio optimization under realistic market conditions with frictions. We frame this issue as a Quadratic Unconstrained Binary Optimization (QUBO) problem, compatible with digital computing and ready for quantum computing, to establish a reliable benchmark. By applying the latest solvers to real data, we release benchmarks that help verify true advancements in dynamic trading strategies, either quantum or digital computing, ensuring that reported improvements in portfolio optimization are based on robust, transparent, and comparable metrics.