Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in the modern quantum devices. Here we present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization: First, we apply the amplitude encoding scheme to the Cart-Pole problem; Second, we propose a hybrid framework where the quantum RL agents are equipped with hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs with dimensions exceeding the number of qubits. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs. We demonstrate the quantum advantage of parameter saving using the amplitude encoding. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

中文翻译：

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通过进化优化的变分量子强化学习

经典强化学习 (RL) 和量子计算 (QC) 的最新进展为在量子计算机上执行 RL 指明了一个有希望的方向。然而，量子强化学习中的潜在应用受到现代量子设备中可用量子位数量的限制。在这里，我们展示了两个使用无梯度进化优化的深度量子 RL 任务框架：首先，我们将幅度编码方案应用于 Cart-Pole 问题；其次，我们提出了一个混合框架，其中量子 RL 代理配备了混合张量网络变分量子电路 (TN-VQC) 架构，以处理维度超过量子位数量的输入。这使我们能够在具有 147 维输入的 MiniGrid 环境上执行量子强化学习。我们展示了使用幅度编码保存参数的量子优势。