Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning

TL;DR

Q-Policy introduces a quantum-classical hybrid framework that leverages quantum superposition and parallelism to accelerate policy evaluation in reinforcement learning, offering a promising theoretical foundation for scalable quantum-enhanced RL.

Contribution

It presents a novel quantum-enhanced policy iteration algorithm with provable sample complexity reductions, validated through classical emulations demonstrating proof-of-concept behavior.

Findings

Quantum encoding enables simultaneous evaluation of multiple state-action pairs.

The algorithm achieves polynomial reductions in sample complexity.

Proof-of-concept validation on classical emulations of control tasks.

Abstract

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.