Quantum computers are expected to offer significant advantages in solving complex optimization problems that are challenging for classical computers. Quadratic Unconstrained Binary Optimization (QUBO) problems represent an important class of problems with relevance in finance and logistics. The Quantum Approximate Optimization Algorithm (QAOA) is a prominent candidate for solving QUBO problems on near-term quantum devices. In this paper, we investigate the performance of both the standard QAOA and the adaptive derivative assembled problem tailored QAOA (ADAPT-QAOA) to solve QUBO problems of varying sizes and hardnesses with a focus on its practical applications in financial feature selection problems. Our main observation is that ADAPT-QAOA significantly outperforms QAOA with hard problems (trade-off parameter α = 0.6) when comparing approximation ratio and time-to-solution. However, the standard QAOA remains efficient for simpler problems. Additionally, we investigate the practical feasibility and limitations of QAOA by scaling analysis based on the real-device calibration data for various hardware platforms. Our estimates indicate that standard QAOA implemented on superconducting quantum computers provides a shorter time-to-solution compared to trapped-ion devices. However, trapped-ion devices are expected to yield more favorable error rates. Our findings provide a comprehensive overview of the challenges, trade-offs, and strategies for deploying QAOA-based methods on near-term quantum hardware.
Pihkakoski et al. (Tue,) studied this question.