Related papers: Novel implementations for reservoir computing -- f…
Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to…
Skyrmions are stable topological textures that have garnered substantial attention within the ferroelectric community for their exotic functional properties. While previous studies have questioned the feasibility of [001]$_{\text{pc}}$…
Physical Reservoir Computing (PRC) is an unconventional computing paradigm, which exploits nonlinear dynamics of reservoir blocks to perform recognition and classification tasks. Here we show with simulations that patterned thin films…
In the present research, we demonstrate the usage of plasmonic effects in thin film structures to control magnetic topological textures, specifically skyrmions and skyrmioniums. We investigate numerically the generation and alteration of…
Magnetic skyrmions are textures behaving as quasiparticles which are topologically different from other states. Their discovery in systems with broken inversion symmetry sparked the search for materials containing such magnetic phase at…
Topology is a powerful tool for categorizing magnetization textures by defining a topological index in both two-dimensional (2D) systems, such as thin films or curved surfaces, and in 3D bulk systems. In the emerging field of 3D…
Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input…
Skyrmions--topologically protected nanoscale spin textures with vortex-like configurations--hold transformative potential for ultra-dense data storage, spintronics and quantum computing. However, their practical utility is challenged by…
Reservoir computing is a novel machine learning algorithm that uses a nonlinear dynamical system to efficiently learn complex temporal patterns from data. The objective of this thesis is to investigate the principles of reservoir computing…
We explore the interplay between two emerging paradigms: reservoir computing and quantum computing. We observe how quantum systems featuring beyond-classical correlations and vast computational spaces can serve as non-trivial,…
Characterized by bulk Dirac or Weyl cones and surface Fermi-arc states, topological semimetals have sparked enormous research interest in recent years. The nanostructures, with large surface-to-volume ratio and easy field-effect gating,…
Topological spin textures in magnetic materials such as skyrmions and hopfions are interesting manifestations of geometric structures in real materials, concurrently having potential applications as information carriers. In the crystalline…
Magnetic skyrmions are topologically protected chiral spin textures which present opportunities for next-generation magnetic data storage and logic information technologies. The topology of these structures originates in the geometric…
Topologically stable structures include vortices in a wide variety of matter, such as skyrmions in ferro- and antiferromagnets, and hedgehog point defects in liquid crystals and ferromagnets. These are characterized by integer-valued…
The notion of non-trivial topological winding in condensed matter systems represents a major area of present-day theoretical and experimental research. Magnetic materials offer a versatile platform that is particularly amenable for the…
Skyrmions have attracted rapidly growing interest due to their topological properties and unique aspects for potential novel applications such as data storage and soft robotics. They can also serve as key elements for materials by design,…
In-materio computing exploits the intrinsic physical dynamics of materials to perform complex computations, enabling low-power, real-time data processing by embedding computation directly within physical layers. Here, we demonstrate a…
Efficient and accurate prediction of physical systems is important even when the rules of those systems cannot be easily learned. Reservoir computing, a type of recurrent neural network with fixed nonlinear units, is one such prediction…
Within the racetrack memory paradigm, systems exploiting magnetic guiding potentials instead of geometrical ones, allow for enhancing the versatility of the final devices adding magnetic reconfigurable capabilities. Hard/soft magnetic…
Optical skyrmions have recently been constructed by tailoring electric or spin field distributions through the interference of multiple surface plasmon polaritons, offering promising features for advanced information processing, transport…