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

2025-02-06

AI Summary

Market frictions—such as transaction costs, latency, and liquidity constraints—hinder fair, realistic benchmarking of quantum and classical algorithms for large-scale dynamic portfolio optimization. Method: We propose the first reproducible, multi-dimensional quantum/classical benchmarking framework, unifying the optimization problem as a Quadratic Unconstrained Binary Optimization (QUBO) formulation. The framework integrates D-Wave quantum annealing with state-of-the-art classical solvers (e.g., Gurobi) and employs a backtesting infrastructure built on real-world high-frequency financial data. Contribution/Results: This work establishes the first standardized evaluation at the thousand-asset scale under realistic market frictions. We release an open-source benchmark dataset and comprehensive performance metrics—including Sharpe ratio, realized return, risk-adjusted return, and execution efficiency. Empirical results demonstrate that current quantum hardware does not yet outperform optimal classical methods. Our framework provides a critical, empirically grounded assessment paradigm bridging theoretical quantum finance and practical implementation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• Benchmarking quantum vs classical computing
• Large-scale dynamic portfolio optimization
• Quadratic Unconstrained Binary Optimization (QUBO)

Innovation

• Quantum computing benchmarking
• QUBO problem framing
• Real data solvers application