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Abstract A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Booleank-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines. 

Abstract Transition metal trichalcogenides (TMTs) are two-dimensional (2D) systems with quasi-one-dimensional (quasi-1D) chains. These 2D materials are less susceptible to undesirable edge defects, which enhances their promise for low-dimensional optical and electronic device applications. However, so far, the performance of 2D devices based on TMTs has been hampered by contact-related issues. Therefore, in this review, a diligent effort has been made to both elucidate and summarize the interfacial interactions between gold and various TMTs, namely, In 4 Se 3 , TiS 3 , ZrS 3 , HfS 3 , and HfSe 3 . X-ray photoemission spectroscopy data, supported by the results of electrical transport measurements, provide insights into the nature of interactions at the Au/In 4 Se 3 , Au/TiS 3 , Au/ZrS 3 , Au/HfS 3 , and Au/HfSe 3 interfaces. This may help identify and pave a path toward resolving the contemporary contact-related problems that have plagued the performance of TMT-based nanodevices. Graphical abstract I – V characteristics of (a) TiS3, (b) ZrS3, and (c) HfS3 

Abstract Magnonicsis a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern information technologies, sensing applications and advanced computational schemes. Spin waves, also known as magnons, carry spin angular momenta that allow for the transmission, storage and processing of information without moving charges. In integrated circuits, magnons enable on-chip data processing at ultrahigh frequencies without the Joule heating, which currently limits clock frequencies in conventional data processors to a few GHz. Recent developments in the field indicate that functional magnonic building blocks for in-memory computation, neural networks and Ising machines are within reach. At the same time, the miniaturization of magnonic circuits advances continuously as the synergy of materials science, electrical engineering and nanotechnology allows for novel on-chip excitation and detection schemes. Such circuits can already enable magnon wavelengths of 50 nm at microwave frequencies in a 5G frequency band. Research into non-charge-based technologies is urgently needed in view of the rapid growth of machine learning and artificial intelligence applications, which consume substantial energy when implemented on conventional data processing units. In its first part, the 2024 Magnonics Roadmap provides an update on the recent developments and achievements in the field of nano-magnonics while defining its future avenues and challenges. In its second part, the Roadmap addresses the rapidly growing research endeavors on hybrid structures and magnonics-enabled quantum engineering. We anticipate that these directions will continue to attract researchers to the field and, in addition to showcasing intriguing science, will enable unprecedented functionalities that enhance the efficiency of alternative information technologies and computational schemes.