QTRL：通过量子训练实现实用的量子强化学习
QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train
AI总结：
QTRL利用Quantum-Train方法以多对数级参数缩减训练经典策略网络，消除数据编码问题并实现仅需经典计算机的低延迟推理，为强化学习提供实用且低成本的量子训练方案。
摘要：
量子强化学习利用量子层在机器学习模型中处理信息。然而，纯量子强化学习和混合量子强化学习都面临数据编码以及推理阶段需要使用量子计算机等挑战。我们将Quantum-Train方法应用于强化学习任务，称为QTRL，使用具有多对数级参数缩减的量子机器学习模型来训练经典策略网络模型。这种QTRL方法消除了传统量子机器学习的数据编码问题，并减少了相应经典策略网络的训练参数。最重要的是，QTRL的训练结果是一个经典模型，这意味着推理阶段只需要经典计算机。这对于强化学习任务来说极其实用且具有成本效益，因为策略模型的低延迟反馈至关重要。
英文摘要：
Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face challenges such as data encoding and the use of quantum computers during the inference stage. We apply the Quantum-Train method to reinforcement learning tasks, called QTRL, training the classical policy network model using a quantum machine learning model with polylogarithmic parameter reduction. This QTRL approach eliminates the data encoding issues of conventional quantum machine learning and reduces the training parameters of the corresponding classical policy network. Most importantly, the training result of the QTRL is a classical model, meaning the inference stage only requires classical computer. This is extremely practical and cost-efficient for reinforcement learning tasks, where low-latency feedback from the policy model is essential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
