A comprehensive survey of quantum computing approaches for portfolio optimization in FinTech

This survey presents a system-level architectural analysis of quantum computing for portfolio optimization, an NP-hard problem in FinTech facing scalability challenges in classical cloud infrastructures. Based on a systematic review of 30 representative papers (2020-2025), three dominant paradigms—Quantum Annealing (QA), Variational Quantum Algorithms (VQAs), and Quantum Machine Learning (QML)—are evaluated specifically by focusing on their constraint enforcement strategies and latency profiles . The analysis suggests that QA currently demonstrates significant near-term operational readiness for constraint-heavy portfolios due to its native solvers and lower communication overhead compared to iterative gate-based methods. Conversely, while VQAs offer high expressivity via mixers, they are operationally limited by the communication complexity of hybrid loops over public networks. Furthermore, QML approaches are shown to shift the bottleneck from optimization to state preparation, where data encoding latency often negates inference advantages. These engineering insights are synthesized into a practical implementation roadmap, advocating for distributed hybrid architectures to bridge the gap between theoretical quantum advantage and low-latency financial deployment.