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Thermal skyrmion diffusion applied in probabilistic computing

Profile image of Daniele PinnaDaniele Pinna

2018, arXiv (Cornell University)

Abstract

Thermally activated processes are key to understanding the dynamics of physical systems. Thermal diffusion of (quasi-)particles for instance not only yields information on transport and dissipation processes but is also an exponentially sensitive tool to reveal emergent system properties and enable novel applications such as probabilistic computing. Here we probe the thermal dynamics of topologically stabilized magnetic skyrmion quasi-particles. We demonstrate in a specially tailored low pinning multilayer material system pure 2 skyrmion diffusion that dominates the dynamics. Finally, we analyse the applicability to probabilistic computing by constructing a device, which uses the thermally excited skyrmion dynamics to reshuffle a signal. Such a skyrmion reshuffler is the key missing component for probabilistic computing and by evaluating its performance, we demonstrate the functionality of our device with high fidelity thus enabling probabilistic computing.

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Thermally activated processes are key to understanding the dynamics of physical systems. Thermal diffusion of (quasi-)particles for instance not only yields information on transport and dissipation processes but is also an exponentially sensitive tool to reveal emergent system properties and enable novel applications such as probabilistic computing. Here we probe the thermal dynamics of topologically stabilized magnetic skyrmion quasi-particles. We demonstrate in a specially tailored low pinning multilayer material system pure 2 skyrmion diffusion that dominates the dynamics. Finally, we analyse the applicability to probabilistic computing by constructing a device, which uses the thermally excited skyrmion dynamics to reshuffle a signal. Such a skyrmion reshuffler is the key missing component for probabilistic computing and by evaluating its performance, we demonstrate the functionality of our device with high fidelity thus enabling probabilistic computing.

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