Machine Learning for maximizing the memristivity of single and coupled quantum memristors

This device exhibits rich nonlinear properties and it is distinguished by a pinched hysteresis curve in the current-voltage (I/V) plane, which can be described by Kubo's response theory [3]. Since the experimental implementation of a memristor in a doped semiconductor by HP Labs in 2008 [4], memristors have garnered significant interest in several areas, including analog computing [5] and neuromorphic computing [6]. A notable application of memristors is the design of devices that mimic biological neural synapses [7] and neural networks [8]. Furthermore, memristor-enabled neuromorphic computing goes beyond the traditional von Neumann computing paradigm, avoiding the von Neumann bottleneck, which is one of the fundamental limitations of current classical computers [9, 10, 11]. Quantum computing [12] aims to revolutionize computation by exploiting exclusively quantum phenomena to surpass the capabilities of classical computers, as we can see from recent breakthroughs [13, 14, 15, 16, 17].