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原文摘要

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. In this paper, we take a step toward this goal and introduce a new QRL framework in which both the policy and the environment are modeled by conditional quantum channels. We then propose a quantum policy-gradient algorithm for parameterized conditional quantum channels with a trainable control basis. We also derive finite- and infinite-horizon gradient estimators using a new parameter-shift rule. This rule is needed because policy parameters are shared across repeated interaction blocks and the infinite-horizon objective is not a finite circuit; rather than shifting every occurrence and evaluating the whole trajectory, our estimator shifts only one sampled policy block. Sampling this block from a geometric distribution yields an unbiased gradient estimator under bounded rewards. Finally, we establish convergence of the resulting stochastic gradient-ascent procedure to stationary points under standard smoothness and step-size assumptions.