A New Framework for Quantum Reinforcement Learning

arXiv:2611.14731v1 Announce Type: new Abstract: Quantum reinforcement learning (QRL) has emerged as a promising approach for controlling quantum systems and accelerating learning tasks with quantum resources. However, most existing QRL formulations still retain a classical reinforcement learning interface: states and actions are often encoded in computational bases and rewards are extracted through measurements. This interface is restrictive for genuinely quantum environments: choosing a measurement can discard entanglement information, while repeated intermediate measurements collapse the evolving state and require costly re-preparation. This motivates a fully QRL framework in which the agent, environment, rewards, and return accumulation are all quantum.