Masahito Mochizuki

Magnetic skyrmions (MSks) and optical skyrmions (OSks) embody topology in matter and in light, respectively. Here we investigate the interaction between a single MSk and an OSk beam. Three distinct nonlinear dynamical modes are identified: rotation, skipping, and trochoidal motion. By decomposing the optical driving force into gradient, orbital-angular-momentum, and spin-angular-momentum contributions, we clarify their respective roles of radial confinement, azimuthal drift, and precessional modulation. The skipping motion arises from the azimuthal asymmetry of the OSk beam and exhibits spatial selectivity originating from the magnetization-polarization coupling between the MSk and OSk. In three dimensions, the coupling acquires a propagation-dependent phase dominated by the differential Gouy phase, which yields $z$-asymmetric skipping trajectories. These results bridge topological particles and topological fields within a unified framework, offering helicity-selective and phase-programmable routes to optomagnonic control.

Dirac magnons, the bosonic counterparts of Dirac fermions in graphene, provide a versatile platform to explore symmetry-protected band crossings and quantum geometry in magnetic insulators while promising high-velocity, low-dissipation spin transport for next-generation magnonic technologies. However, their stability under realistic, finite-temperature conditions remains an open question. Here, we develop a microscopic theory of thermal magnon-magnon interactions in van der Waals honeycomb ferromagnets, focusing on both gapless and gapped Dirac magnons. Using nonlinear spin-wave theory with magnon self-energy corrections and a T -matrix resummation that captures two-magnon bound states, we quantitatively reproduce temperature- and momentum-dependent energy shifts and linewidths observed experimentally in the gapless Dirac magnon material CrBr 3 , even near the Curie temperature. Our approach provides a consistent interpretation of theoretical predictions and experiment shedding light on the role of bound states in enhancing magnon damping at low temperatures. For gapped Dirac magnon materials such as CrI 3 , CrSiTe 3 , and CrGeTe 3 , we find a thermally induced reduction of the topological magnon gap, while no indication of thermally driven topological phase transitions is observed within the considered parameter range. Classical atomistic spin-dynamics simulations corroborate the gap’s robustness up to the Curie temperature. Furthermore, we establish a practical criterion for observing topological gaps by determining the minimum ratio of Dzyaloshinskii-Moriya interaction to Heisenberg exchange required to overcome thermal broadening throughout the ordered phase, typically around 5%. Taken together, our results elucidate the interplay between thermal many-body effects and topology in low-dimensional magnetic systems and provide a controlled framework for the interpretation of spectroscopic measurements.

Information-processing algorithms inspired by survival strategies of slime molds have been intensively studied to realize combinatorial optimization machines with high computational efficiency and low power consumption. However, previously proposed mathematical models are not suitable for physical implementation as they involved constraints due to the volume-conservation law of slime molds, multiple conditional branches, and numerous complex functions such as sigmoid functions. Here, we propose a mathematical model, the Amoeba TSP (traveling salesman problem) recurrence formula model, which simplifies the information-processing procedure and enables physical implementation without sacrificing optimization performance. Specifically, we demonstrate using numerical simulations that (1) the conservation-law constraints assumed in previous models can be removed, (2) several sigmoid functions can be eliminated, and (3) Gaussian-distribution random numbers that can be generated from thermal fluctuations can be employed as random fluctuations. These improvements broaden the range of candidate materials and phenomena for physical implementation and reduce the number of device components required. Furthermore, we discover that our mathematical model integrating these modifications is mathematically equivalent to a recurrent neural network with fixed weights that exploits nonlinear dynamics. Remarkably, despite its significant simplification, the proposed model exhibits superior solution-search performance compared with the previous models in terms of solution quality, convergence speed, and scalability. Our results open a practical pathway toward realization of highly efficient and high-performance combinatorial optimization machines based on non-von Neumann-type architectures and provide insights into bioinspired information processing by uncovering a connection between the computational principles of slime molds and recurrent neural networks.

Particle-like chiral magnetic skyrmions can flow in nanotracks and behave like chiral fluids. Using interacting flows to perform logical operations is an important topic in microfluidics and nanofluidics. Here, we report a basic nanofluidic logic computing system based on chiral magnetic skyrmions flowing in parallel pipelines connected by an H-shaped junction. The flow behaviors could be manipulated by adjusting the spin polarization angle, which controls the intrinsic skyrmion Hall angle. We demonstrate that within certain range of the spin polarization angle, fully developed skyrmion flows could lead to fluidic logical operations, which significantly reduce the complexity of skyrmion logic as there is no need for deterministic creation, precise control, and detection of a single isolated skyrmion. Our results suggest that the chiral flow behaviors of magnetic quasiparticles may offer possibilities for spintronic and nanofluidic functions.