Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
This work addresses scalability challenges in reinforcement learning for future quantum computing applications, but it is incremental as it builds on existing quantum-enhanced methods with limited empirical validation.
The authors tackled the problem of scaling reinforcement learning by proposing Q-Policy, a hybrid quantum-classical framework that uses quantum computing to accelerate policy evaluation, achieving provable polynomial reductions in sample complexity in theory, though experiments were limited to small tasks on classical emulations.
We propose Q-Policy, a hybrid quantum-classical reinforcement learning (RL) framework that mathematically accelerates policy evaluation and optimization by exploiting quantum computing primitives. Q-Policy encodes value functions in quantum superposition, enabling simultaneous evaluation of multiple state-action pairs via amplitude encoding and quantum parallelism. We introduce a quantum-enhanced policy iteration algorithm with provable polynomial reductions in sample complexity for the evaluation step, under standard assumptions. To demonstrate the technical feasibility and theoretical soundness of our approach, we validate Q-Policy on classical emulations of small discrete control tasks. Due to current hardware and simulation limitations, our experiments focus on showcasing proof-of-concept behavior rather than large-scale empirical evaluation. Our results support the potential of Q-Policy as a theoretical foundation for scalable RL on future quantum devices, addressing RL scalability challenges beyond classical approaches.