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Category: computing – Page 218

Tuning skyrmion helicity for racetrack memory and quantum computing applications

Three distinct topological degrees of freedom are used to define all topological spin textures based on out-of-plane and in-plane spin configurations: the topological charge, representing the number of times the magnetization vector m wraps around the unit sphere; the vorticity, which quantifies the angular integration of the magnetic moment along the circumferential direction of a domain wall; and the helicity, defining the swirling direction of in-plane magnetization.

Electrical manipulation of these three degrees of freedom has garnered significant attention due to their potential applications in future spintronic devices. Among these, the helicity of a magnetic skyrmion—a critical topological property—is typically determined by the Dzyaloshinskii-Moriya interaction (DMI). However, controlling skyrmion helicity remains a formidable challenge.

A team of scientists led by Professor Yan Zhou from The Chinese University of Hong Kong, Shenzhen, and Professor Senfu Zhang from Lanzhou University successfully demonstrated a controllable helicity switching of skyrmions using spin-orbit torque, enhanced by thermal effects.

Google says its new quantum chip indicates that multiple universes exist

Google on Monday announced Willow, its latest, greatest quantum computing chip. The speed and reliability performance claims Google’s made about this chip were newsworthy in themselves, but what really caught the tech industry’s attention was an even wilder claim tucked into the blog post about the chip.

Google Quantum AI founder Hartmut Neven wrote in his blog post that this chip was so mind-boggling fast that it must have borrowed computational power from other universes.

Ergo the chip’s performance indicates that parallel universes exist and “we live in a multiverse.”

Quantum computing’s next step: New algorithm boosts multitasking

Quantum computers differ fundamentally from classical ones. Instead of using bits (0s and 1s), they employ “qubits,” which can exist in multiple states simultaneously due to quantum phenomena like superposition and entanglement.

For a quantum computer to simulate dynamic processes or process data, among other essential tasks, it must translate complex input data into “quantum data” that it can understand. This process is known as quantum compilation.

Essentially, quantum compilation “programs” the quantum computer by converting a particular goal into an executable sequence. Just as the GPS app converts your desired destination into a sequence of actionable steps you can follow, quantum compilation translates a high-level goal into a precise sequence of quantum operations that the quantum computer can execute.

Graphene Interconnects to Moore’s Law’s Rescue

The semiconductor industry’s long held imperative—Moore’s Law, which dictates that transistor densities on a chip should double roughly every two years—is getting more and more difficult to maintain. The ability to shrink down transistors, and the interconnects between them, is hitting some basic physical limitations. In particular, when copper interconnects are scaled down, their resistivity skyrockets, which decreases how much information they can carry and increases their energy draw.

The industry has been looking for alternative interconnect materials to prolong the march of Moore’s Law a bit longer. Graphene is a very attractive optionin many ways: The sheet-thin carbon material offers excellent electrical and thermal conductivity, and is stronger than diamond.

However, researchers have struggled to incorporate graphene into mainstream computing applications for two main reasons. First, depositing graphene requires high temperatures that are incompatible with traditional CMOS manufacturing. And second, the charge carrier density of undoped, macroscopic graphene sheets is relatively low.

Making smaller transistors, and the interconnections between them, is getting near impossible. Copper interconnects get more resistive as they are scaled down, making them worse and slower at carrying information. Startup Destination 2D thinks graphene is the solution. They have a novel technique of growing graphene that is CMOS compatible, promising 100x current density improvement over copper.

Beyond Silicon: How DNA Is Powering Next-Gen Computers

Researchers have developed a new, fast, and rewritable method for DNA computing that promises smaller, more powerful computers.

This method mimics the sequential and simultaneous gene expression in living organisms and incorporates programmable DNA circuits with logic gates. The improved process places DNA on a solid glass surface, enhancing efficiency and reducing the need for manual transfers, culminating in a 90-minute reaction time in a single tube.

Advancements in DNA-Based Computation.

Leaner Large Language Models could enable Efficient Local Use on Phones and Laptops

Large language models (LLMs) are increasingly automating tasks like translation, text classification and customer service. But tapping into an LLM’s power typically requires users to send their requests to a centralized server—a process that’s expensive, energy-intensive and often slow.

Now, researchers have introduced a technique for compressing an LLM’s reams of data, which could increase privacy, save energy and lower costs. Their findings are published on the arXiv preprint server.

The new algorithm, developed by engineers at Princeton and Stanford Engineering, works by trimming redundancies and reducing the precision of an LLM’s layers of information. This type of leaner LLM could be stored and accessed locally on a device like a phone or laptop and could provide performance nearly as accurate and nuanced as an uncompressed version.

Ultrafast Control of Nonlinear Hot Dirac Electrons in Graphene: An International Collaboration

Nonlinear optics explores how powerful light (e.g. lasers) interacts with materials, resulting in the output light changing colour (i.e. frequency) or behaving differently based on the intensity of the incoming light. This field is crucial for developing advanced technologies such as high-speed communication systems and laser-based applications. Nonlinear optical phenomena enable the manipulation of light in novel ways, leading to breakthroughs in fields like telecommunications, medical imaging, and quantum computing. Two-dimensional (2D) materials, such as graphene—a single layer of carbon atoms in a hexagonal lattice—exhibit unique properties due to their thinness and high surface area. Graphene’s exceptional electronic properties, related to relativistic-like Dirac electrons and strong light-matter interactions, make it promising for nonlinear optical applications, including ultrafast photonics, optical modulators, saturable absorbers in ultrafast lasers, and quantum optics.

Dr. Habib Rostami, from the Department of Physics at the University of Bath, has co-authored pioneering research published in Advanced Science. This study involved an international collaboration between an experimental team at Friedrich Schiller University Jena in Germany and theoretical teams at the University of Pisa in Italy and the University of Bath in the UK. The research aimed to investigate the ultrafast opto-electronic and thermal tuning of nonlinear optics in graphene.

This study discovers a new way to control high-harmonic generation in a graphene-based field-effect transistor. The team investigated the impact of lattice temperature, electron doping, and all-optical ultrafast tuning of third-harmonic generation in a hexagonal boron nitride-encapsulated graphene opto-electronic device. They demonstrated up to 85% modulation depth along with gate-tuneable ultrafast dynamics, a significant improvement over previous static tuning. Furthermore, by changing the lattice temperature of graphene, the team could enhance the modulation of its optical response, achieving a modulation factor of up to 300%. The experimental fabrication and measurement took place at Friedrich Schiller University Jena. Dr. Rostami played a crucial role in the study by crafting theoretical models. These models were developed in collaboration with another theory team at the University of Pisa to elucidate new effects observed in graphene.

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