Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging from energy-efficient memories to physical neural networks and neuromorphic computing platforms. Recently, the concept of a quantum memristor was introduced by a few proposals, all of which face limited technological practicality. Here we propose and experimentally demonstrate a novel quantum-optical memristor (based on integrated photonics) that acts on single-photon states. We fully characterize the memristive dynamics of our device and tomographically reconstruct its quantum output state. Finally, we propose a possible application of our device in the framework of quantum machine learning through a scheme of quantum reservoir computing, which we apply to classical and quantum learning tasks. Our simulations show promising results, and may break new ground towards the use of quantum memristors in quantum neuromorphic architectures.

忆阻器件是一类具有历史相关动力学的物理系统，其特征在于其输入-输出关系中的特征磁滞回线。在过去的几十年里，忆阻器件引起了人们对电子产品的极大兴趣。这是因为忆阻动力学在纳米级设备中非常普遍，并且具有潜在的突破性应用，从节能存储器到物理神经网络和神经形态计算平台。最近，一些提案引入了量子忆阻器的概念，但都面临着有限的技术实用性。在这里，我们提出并通过实验证明了一种作用于单光子状态的新型量子光学忆阻器（基于集成光子学）。我们充分表征了我们设备的忆阻动力学，并在断层扫描上重建了它的量子输出状态。最后，我们通过量子库计算方案提出了我们的设备在量子机器学习框架中的可能应用，我们将其应用于经典和量子学习任务。我们的模拟显示出有希望的结果，并可能为在量子神经形态架构中使用量子忆阻器开辟新天地。