نبذة مختصرة : The limitations of the traditional von Neumann computing architecture, particularly evident in the slowdown of Moore's law, have spurred the development of alternative domain-specific computing paradigms. This dissertation explores novel materials-physics based solutions for two promising alternatives: quantum computing and probabilistic computing, with a specific focus on leveraging magnetic spin textures and their unique properties. We demonstrate that magnetic spin textures, with their inherent topology and chirality, offer distinctive advantages in addressing key challenges in both computing paradigms. These textures' ability to couple with various degrees of freedom, such as electrical, thermal, mechanical, and optical, makes them particularly suitable for hybrid device implementations. Our work presents four contributions to the field. First, we propose a novel approach of using skyrmions --- topologically protected rigid-object like spin textures --- to nucleate and braid Majorana modes in topological superconductor-magnetic multilayer heterostructures. We show analytically and numerically that skyrmion--vortex bound pairs can be braided in experimentally relevant timescales. Inspired by circuit quantum electrodynamics methods, we propose a novel readout scheme based on the dispersive coupling between vortex confinement states and Majorana bound states. This work paves the way for experimentally demonstrating the non-Abelian statistics of Majorana bound states, which might be a crucial step towards the development of fault-tolerant topological quantum computers. Second, we study thermal relaxation mechanisms and timescales of spin-split chiral antiferromagnets. The class of spin-split antiferromagnets, including altermagnets, have recently emerged as excellent candidates for ultra-fast and low-energy spintronics applications. Due the lack of dipolar order, they are unaffected by stray fields. However, the spin-split bands still offer electrical control and readout of these antiferromagnets unlike the ...
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