Dipartimento di Fisica, Università della Calabria, 87036, Arcavacata di Rende, (CS), Italy.
Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC, Campus Universitat Illes Balears, 07122, Palma de Mallorca, Spain.
Sci Rep. 2023 Mar 8;13(1):3913. doi: 10.1038/s41598-023-30990-5.
We introduce a classical-quantum hybrid approach to computation, allowing for a quadratic performance improvement in the decision process of a learning agent. Using the paradigm of quantum accelerators, we introduce a routine that runs on a quantum computer, which allows for the encoding of probability distributions. This quantum routine is then employed, in a reinforcement learning set-up, to encode the distributions that drive action choices. Our routine is well-suited in the case of a large, although finite, number of actions and can be employed in any scenario where a probability distribution with a large support is needed. We describe the routine and assess its performance in terms of computational complexity, needed quantum resource, and accuracy. Finally, we design an algorithm showing how to exploit it in the context of Q-learning.
我们引入了一种经典-量子混合计算方法，使得学习代理的决策过程中的性能提高了两倍。利用量子加速器的范例，我们引入了一种在量子计算机上运行的例程，该例程允许概率分布的编码。然后，在强化学习设置中，我们使用此量子例程来编码驱动动作选择的分布。在动作数量虽然有限但很大的情况下，我们的例程非常适用，并且可以在需要具有很大支持的概率分布的任何情况下使用。我们描述了例程，并根据计算复杂性、所需的量子资源和准确性来评估其性能。最后，我们设计了一种算法，展示了如何在 Q-learning 的上下文中利用它。
