Lifeboat Foundation

Quantum computers hold the promise to emulate complex materials, helping researchers better understand the physical properties that arise from interacting atoms and electrons. This may one day lead to the discovery or design of better semiconductors, insulators, or superconductors that could be used to make ever faster, more powerful, and more energy-efficient electronics.

The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for devising AI for their respective fields of study. KAIST researchers have now succeeded in dramatically shortening the calculation time of highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel approach to teach AI.

For the first time, EPFL researchers have directly observed molecules engaging in hydrogen bonds within liquid water, capturing electronic and nuclear quantum effects that had previously been accessible only through theoretical simulations.

Water is synonymous with life, but the dynamic, multifaceted interaction that brings H₂O molecules together – the hydrogen bond – remains mysterious. These hydrogen bonds form as hydrogen and oxygen atoms from neighboring water molecules connect, exchanging electronic charge in the process.

This charge-sharing is a key feature of the three-dimensional ‘H-bond’ network that gives liquid water its unique properties, but quantum phenomena at the heart of such networks have thus far been understood only through theoretical simulations.

A U.S. Naval Research Laboratory (NRL) multi-disciplinary team developed a new paradigm for the control of quantum emitters, providing a new method for modulating and encoding quantum photonic information on a single photon light stream.

The discovery of the quantum tunneling (QT) effect—the transmission of particles through a high potential barrier—was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a deep neural network (DNN) architecture that processes information using the effect of QT. I demonstrate the ability of QT-DNN to recognize optical illusions like a human. Tasking QT-DNN to simulate human perception of the Necker cube and Rubin’s vase, I provide arguments in favor of the superiority of QT-based activation functions over the activation functions optimized for modern applications in machine vision, also showing that, at the fundamental level, QT-DNN is closely related to biology-inspired DNNs and models based on the principles of quantum information processing.

A scheme that moves electromagnetically trapped ions around a 2D array of sites could aid development of scaled-up ion-based quantum computing.

Arrays of ions held in electromagnetic traps could eventually become powerful quantum computers, but as the number of ions increases, linear arrays become impractical. Rearranging the ions to achieve interactions between any specific pair becomes challenging, but now researchers have demonstrated a 2D scheme that does it more efficiently [1]. Using this approach, the full range of quantum operations is feasible with relatively simple applied voltages, and the researchers believe that it should soon find use in practical ion-based devices.

In trapped-ion quantum processors, single ions represent quantum bits (qubits). One of the main advantages of this technology is that individual ions can be moved around, says Robert Delaney of Quantinuum, a quantum-computing company. If rearranging ions—known as sorting—can bring every ion close enough to every other ion to allow pairwise quantum entanglement, the system has what is called all-to-all connectivity.

The standard model of fundamental particles and interactions has now been in place for about a half-century. It has successfully passed experimental test after experimental test at particle accelerators. However, many of the model’s features are poorly understood, and it is now clear that standard-model particles only compose about 5% of the observed energy density of the Universe. This situation naturally encourages researchers to look for new particles and interactions that fall outside this model. One way to perform this search is to prepare a gas of polarized atoms and to look for changes in this polarization that might come from new physics. Haowen Su from the University of Science and Technology of China and colleagues have used two separated samples of polarized xenon gas to probe spin-dependent interactions [1] (Fig. 1). The results place constraints on axions—a candidate for dark matter—in a theoretically favored mass range called the axion window.

Searches for new spin-dependent interactions have exploded over the past decade. Special relativity and quantum mechanics tightly constrain the mathematical form for such interactions, with the main adjustable parameters being the coupling strength and the spatial range. Since the form of these interactions is generic across many models, it is possible to conduct experimental searches for new interaction signatures, even in the absence of a specific theory for beyond-standard-model physics.

Imperial researchers have proposed a new way to directly probe quantum entanglement, the effect that led to the puzzling concept of “spooky action at a distance,” where previously grouped particles’ quantum states cannot be described independently of each other. The research has been accepted for publication in Physical Review X.

Radio frequency (RF) and microwave power measurements are widely used to support applications across space, defense, and communication. These precise measurements enable engineers to accurately characterize waveforms, components, circuits, and systems.