Related papers: Predicting the future with magnons
Physical reservoir computing is a computational framework that offers an energy- and computation-efficient alternative to conventional training of neural networks. In reservoir computing, input signals are mapped into the high-dimensional…
Spin waves, the collective excitations of the magnetic order parameter, and magnons, the associated quasiparticles, are envisioned as possible data carriers in future wave-based computing architectures. On the road towards spin-wave…
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for…
Hybrid systems combining magnons and superconducting quantum circuits have attracted increasing interest in recent years. Magnonic crystals (MCs) are one of the building blocks of room-temperature magnonics and can be used to create devices…
In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the…
We experimentally and theoretically demonstrate that nonlinear spin-wave dynamics can induce an effective resonant interaction between non-resonant magnon modes in a yttrium iron garnet disk. Under strong pumping near the ferromagnetic…
Spintronics is concerned with replacing charge current with current of spin, the electron's intrinsic angular momentum. In magnetic insulators, spin currents are carried by magnons, the quanta of spin-wave excitations on top of the…
Spin noise spectroscopy is a technique to measure magnetization fluctuations, a subject of increasing relevance in ultrafast spintronics. We investigate numerically the equilibrium spin noise of ferro- and antiferromagnets within an…
Magnon spintronics is an emerging field that explores the use of magnons, the quanta of spin waves in magnetic materials for information processing and communication. Achieving unidirectional information transport with fast switching…
In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting candidates for RC due to their huge state space, nonlinear…
Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals. Its hardware implementations have received much attention because of their simplicity and remarkable performance on a series of benchmark tasks.…
An emerging computing paradigm, so-called next-generation reservoir computing (NG-RC) is investigated. True to its namesake, NG-RC requires no actual reservoirs for input data mixing but rather computing the polynomial terms directly from…
Cavity magnomechanics using mechanical degrees of freedom in ferromagnetic crystals provides a powerful platform for observing many interesting classical and quantum nonlinear phenomena in the emerging field of magnon spintronics. However,…
Magnons, bosonic quasiparticles carrying angular momentum, can flow through insulators for information transmission with minimal power dissipation. However, it remains challenging to develop a magnon-based logic due to the lack of efficient…
Hybrid dynamic systems have recently gained interests with respect to both fundamental physics and device applications, particularly with their potential for coherent information processing. In this perspective, we will focus on the recent…
The motion of one-hundred point vortices in a circular cylinder is simulated numerically and compared with theoretical predictions based on statistical mechanics. The novel aspect considered here is that the vortices have greatly different…
In the framework of magnonics all-optical femtosecond laser experiments are used to study spin waves and their relaxation paths. Magnonic crystal structures based on antidots allow the control over the spin-wave modes. In these…
The efficient excitation of spin waves is a key challenge in the realization of magnonic devices. We demonstrate the current-driven generation of spin waves in antiferromagnetically coupled magnetic vortices. We employ time-resolved…
The authors have numerically studied how to enhance reservoir computing performance by thoroughly extracting their spin-wave device potential for higher-dimensional information generation. The reservoir device has a 1-input exciter and…
Controlling spin currents, i.e., the flow of spin angular momentum, in small magnetic devices is the principal objective of spin electronics, a main contender for future energy efficient information technologies. Surprisingly, a pure spin…