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A research team led by Director JO Moon-Ho of the Center for Van der Waals Quantum Solids within the Institute for Basic Science (IBS) has implemented a novel method to achieve epitaxial growth of 1D metallic materials with a width of less than 1 nanometer (nm). The group applied this process to develop a new structure for 2D semiconductor logic circuits. Notably, they used the 1D metals as a gate electrode of the ultra-miniaturized transistor.

This research was published in the journal Nature Nanotechnology (“Integrated 1D epitaxial mirror twin boundaries for ultra-scaled 2D MoS 2 field-effect transistors”).

Integrated devices based on two-dimensional (2D) semiconductors, which exhibit excellent properties even at the ultimate limit of material thickness down to the atomic scale, are a major focus of basic and applied research worldwide. However, realizing such ultra-miniaturized transistor devices that can control the electron movement within a few nanometers, let alone developing the manufacturing process for these integrated circuits, has been met with significant technical challenges.

The advent of quantum computers promises to revolutionize computing by solving complex problems exponentially more rapidly than classical computers. However, today’s quantum computers face challenges such as maintaining stability and transporting quantum information.

Phonons, which are quantized vibrations in periodic lattices, offer new ways to improve these systems by enhancing qubit interactions and providing more reliable information conversion. Phonons also facilitate better communication within quantum computers, allowing the interconnection of them in a network.

Nanophononic materials, which are artificial nanostructures with specific phononic properties, will be essential for next-generation quantum networking and communication devices. However, designing phononic crystals with desired vibration characteristics at the nano-and micro-scales remains challenging.

Luminescence refers to the result of a process in which an object absorbs light at one wavelength and then re-emits it at another wavelength. Through light absorption, electrons in the ground state of the material are excited to a higher energy state. After a certain amount of time characteristic of each excited state, the electrons decay to lower energy states, including the ground state, and emit light. The phenomenon is used in a wide array of technological applications involving highly efficient and reproducible emitting devices that can easily be miniaturized.

The materials with the highest luminescence efficiency include quantum dots (QDs), currently used in high-resolution displays, LEDs, solar panels, and sensors of various kinds, such as those used for precision medical imaging. Functionalization of the surface of QDs with various types of molecules permits interaction with cellular structures or other molecules of interest for the purpose of investigating molecular-level biological processes.

QDs are semiconductor nanoparticles whose emissive characteristics are directly linked to dot size, owing to the phenomenon of quantum confinement. For this reason, monitoring and control of crystal growth during synthesis of QDs in solution permits intelligent planning of the desired luminescence.

In the Mpemba effect, a warm liquid freezes faster than a cold one. Three studies investigate quantum versions of this effect, challenging our understanding of quantum thermodynamics.

Under certain conditions, warm water can freeze faster than cold water. This phenomenon was named the Mpemba effect after Erasto Mpemba, a Tanzanian high schooler who described the effect in the 1960s [1]. The phenomenon has sparked intense debates for more than two millennia and continues to do so [2]. Similar processes, in which a system relaxes to equilibrium more quickly if it is initially further away from equilibrium, are being intensely explored in the microscopic world. Now three research teams provide distinct perspectives on quantum versions of Mpemba-like effects, emphasizing the impact of strong interparticle correlations, minuscule quantum fluctuations, and initial conditions on these relaxation processes [3– 5]. The teams’ findings advance quantum thermodynamics and have potential implications for technologies, ranging from information processors to engines, powered by quantum resources.

In top-down strategies, physicists use observations of macroscopic (classical) phenomena to infer fundamental microscopic (quantum) processes; in bottom-up strategies, they use studies of those fundamental processes to predict classical phenomena. Historically, studies of the Mpemba effect began with empirical observations and ad hoc assumptions about the microscopic world. Despite descriptions of the effect by Aristotle and Descartes, and modern attention from Mpemba, the phenomenon has not influenced the field of thermodynamics. The Mpemba effect is complex, lacks a precise definition, and has reproducibility issues. As a result, experimental observations and explanations have been debated for decades without consensus, making the effect often seem like just a curiosity.

Researchers have successfully controlled the quantum mechanical properties of Andreev bound states in bilayer graphene-based Josephson junctions using gate voltage. Their research is published in Physical Review Letters. The research team includes Professors Gil-Ho Lee and Gil Young Cho from the Department of Physics at Pohang University of Science and Technology (POSTECH) in South Korea in collaboration with Dr. Kenji Watanabe and Dr. Takashi Taniguchi from National Institute for Materials Science (NIMS) in Japan.

Superconductors are materials that exhibit zero electrical resistance under specific conditions such as extremely low temperatures or high pressures. When a very thin normal conductor is placed between two superconductors, a supercurrent flows through the normal conductor due to the proximity effect where superconductivity extends into the normal conductor. This device is known as a Josephson junction.

Within the normal conductor, new quantum states called Andreev bound states are formed, which are crucial for mediating the supercurrent flow.

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The development of alternative platforms for computing has been a longstanding goal for physics, and represents a particularly pressing concern as conventional transistors approach the limit of miniaturization. A potential alternative paradigm is that of reservoir computing, which leverages unknown, but highly nonlinear transformations of input-data to perform computations. This has the advantage that many physical systems exhibit precisely the type of nonlinear input-output relationships necessary for them to function as reservoirs. Consequently, the quantum effects which obstruct the further development of silicon electronics become an advantage for a reservoir computer. Here we demonstrate that even the most basic constituents of matter–atoms–can act as a reservoir for computing where all input-output processing is optical, thanks to the phenomenon of High Harmonic Generation.

What is the future of generative AI compute?

— The future of generative AI compute involves embedding AI algorithms into the physics of the world to push the limits of density, spatial efficiency, and speed for AI, creating a full stack of software and hardware specifically designed for AI from first principles.

The first #Quantum #Supercomputers are here! Quantum enabled supercomputing promises to shed light on new quantum algorithms, hardware innovations, and error mitigation schemes. Large collaborations in the field are kicking off between corporations and supercomputing centers. Companies like NVIDIA, IBM, IQM, QuEra, and others are some of the earliest to participate in these partnerships.

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Thermodynamic computing has the potential to revolutionize AI and machine learning by harnessing thermal fluctuations for faster, more efficient, and lower power computing systems Questions to inspire discussion What is the future revolution in computing? —The future revolution in computing is harnessing thermal fluctuations for physics-based computing systems.