Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. This type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

Central European Institute of Technology Brno University of Technology Brno 61200 Czech Republic

Unité Mixte de Physique CNRS Thales Université Paris Saclay Palaiseau 91767 France

Université de Lorraine CNRS Institut Jean Lamour Nancy F 54000 France

Université Grenoble Alpes CNRS Grenoble INP Institut NEEL Grenoble 38000 France

Université Paris Saclay CNRS Centre de Nanosciences et de Nanotechnologies Palaiseau 91120 France

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