Abstract
Variational quantum algorithms represent a promising approach to quantum machine learning where classical neural networks are replaced by parametrized quantum circuits. Here, we present a variational approach to quantize projective simulation (PS), a reinforcement learning model aimed at interpretable artificial intelligence. Decision making in PS is modeled as a random walk on a graph describing the agent's memory. To implement the quantized model, we consider quantum walks of single photons in a lattice of tunable Mach-Zehnder interferometers. We propose variational algorithms tailored to reinforcement learning tasks, and we show, using an example from transfer learning, that the quantized PS learning model can outperform its classical counterpart. Finally, we discuss the role of quantum interference for training and decision making, paving the way for realizations of interpretable quantum learning agents.
Abstract (translated)
变分量子算法在量子机器学习中是一种有前途的方法,其中经典神经网络被参数化的量子电路所取代。在这里,我们提出了一种变分量子算法来量化投射模拟(PS),这是一个旨在可解释人工智能的强化学习模型。在PS中,决策过程被建模为描述代理记忆的图的随机漫步。为了实现量化模型,我们考虑在可调的米哈伊万万诺选举树离散对偶框架中的单个光子量子漫步。我们提出了针对强化学习任务的变分算法,并使用迁移学习一个例子来展示,量化的PS学习模型可以比经典的模型表现更好。最后,我们讨论了量子干涉在训练和决策中的作用,为可解释的量子学习代理的实现铺平了道路。
URL
https://arxiv.org/abs/2301.13669
