论文标题
具有优化的动作解码的量子策略梯度算法
Quantum Policy Gradient Algorithm with Optimized Action Decoding
论文作者
论文摘要
通过变分量子电路（VQC）实施的量子机学习被认为是嘈杂的中间尺度量子计算时代的有前途的概念。为了关注量子增强学习中的应用，我们为量子策略梯度方法提出了一个特定的动作解码程序。我们介绍了一种新颖的质量措施，使我们能够优化受本地和全球量子测量的启发，以优化行动选择所需的经典后处理。所得算法在几个基准环境中表现出显着的性能提高。通过这种技术，我们成功地在5 Qubit的硬件设备上执行了完整的培训例程。我们的方法仅引入了可忽略的经典开销，并且有可能将基于VQC的算法改善量子增强学习领域。
Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on applications in quantum reinforcement learning, we propose a specific action decoding procedure for a quantum policy gradient approach. We introduce a novel quality measure that enables us to optimize the classical post-processing required for action selection, inspired by local and global quantum measurements. The resulting algorithm demonstrates a significant performance improvement in several benchmark environments. With this technique, we successfully execute a full training routine on a 5-qubit hardware device. Our method introduces only negligible classical overhead and has the potential to improve VQC-based algorithms beyond the field of quantum reinforcement learning.
