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.

翻译：变化量子算法代表了量子机器学习的一种很有希望的方法, 古典神经网络被配对的量子电路所取代。 在这里, 我们展示了一种变式方法, 用来量化投影模拟(PS), 这是一种强化的人工智能学习模型。 在 PS 中, 决策的模型是在描述代理人记忆的图表上的随机行走模式。 为了实施量化模型, 我们考虑将单个光子的量子行走放在一个长长长的金枪鱼可塑形马赫- Zehnder 干涉仪中。 我们提出了适合强化学习任务的变式算法, 我们用转移学习的一个例子来显示, 量化的 PS 学习模型可以超越它的经典模型。 最后, 我们讨论了量干扰在培训和决策中的作用, 为实现可解释的量子学习器铺平了道路 。