In-Memory Probabilistic Computing using Gate-tunable Layer Pseudospins in van der Waals Heterostructures

Layer pseudospins, exhibiting quantum coherence and precise multistate controllability, present significant potential for the advancement of future computing technologies. In this work, we propose an in-memory probabilistic computing scheme based on the electrical manipulation of layer pseudospins in layered materials, by exploiting the interaction between real spins and layer pseudospins. This approach demonstrates near-linear time complexity, achieving computation speeds approximately five times faster than conventional probabilistic computing schemes for the Maximum Cut (MAXCUT) optimization problem. Moreover, we demonstrate that our proposed scheme can realize a computation speed comparable to quantum computing when the MAXCUT problem with large number of nodes is considered. Our results highlight the potential of layer degree of freedom as a new information carrier for building up advanced computational systems.