Reinforcement Learning Meets Quantum Optimization: A New Paradigm for Dynamic Portfolio Management

Abstract

We propose and analyze a hybrid paradigm that integrates Reinforcement Learning (RL) with Quantum Optimization (QO) methods for dynamic portfolio management. The approach leverages RL to learn policy structure and market-timing signals, while delegating discrete, combinatorial, and constrained subproblems (e.g., cardinality-constrained selection, rebalancing under transaction limits) to quantum optimization engines such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and quantum annealers via QUBO/HUBO encodings. We develop the theoretical mapping from portfolio selection and rebalancing into Markov Decision Processes (MDPs) and Quadratic Unconstrained Binary Optimization (QUBO) / Higher-Order Binary Optimization (HUBO) problems, present algorithmic architectures for hybrid training and execution, and provide reproducible experimental protocols for benchmarking against classical baselines. We review the state of the art in RL for finance and quantum optimization for combinatorial finance tasks, and we empirically motivate the design choices using recent studies that benchmark quantum heuristics on portfolio problems. Our results and analysis articulate where hybrid RL–QO can offer practical advantages in near-term noisy intermediate-scale quantum (NISQ) environments, what constraints limit current applicability, and a road map for industrial deployment in asset management and robo-advisory contexts.