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

TL;DR

The paper addresses large-scale dynamic portfolio optimization under market frictions by formulating it as a Quadratic Unconstrained Binary Optimization (QUBO)/Ising problem suitable for both classical and quantum solvers. It adopts an adiabatic quantum computing framework and hybrid quantum annealing, mapping the objective into a Hamiltonian $H(s)=A(s)H_I+B(s)H_F$ with $H_I= \sum_i sigma_i^x$ and $H_F= \sum_i h_i sigma_i^z + \sum_{i<j} J_{ij} sigma_i^z sigma_j^z$, and uses minor-embedding onto a Chimera graph for hardware implementation. Empirical results on a 50-stock portfolio over 1–2 weeks show the quantum-hybrid approach achieving lower volatility and a higher Sharpe ratio, with solution times around 60s compared to >600s for classical solvers. The study provides a scalable benchmark up to ~5000 qubits and demonstrates a practical quantum advantage in convergence speed, while acknowledging hardware limitations and proposing a pathway toward broader adoption in finance.

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.