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Researchers have achieved long-distance entanglement between two calcium ions, each of which lies in a different building, showing that trapped ions could be used to create quantum networks.

Among the many candidate platforms for quantum-information applications, trapped-ion qubits are promising because of their long coherence times and their potential for multiqubit operations (see Viewpoint: Trapped Ions Make Impeccable Qubits). Alone, those properties are insufficient for some quantum applications, however: to build quantum communication networks, for example, requires the qubits’ delicate quantum states be shared over long distances. Demonstrations of this ability have been lacking for trapped-ion systems. Now a team led by Benjamin Lanyon at the Institute for Quantum Optics and Quantum Information, Austria, and Tracy Northup at the University of Innsbruck, Austria, have addressed this shortfall by entangling two trapped-ion qubits residing in different buildings [1].

Lanyon, Northup, and colleagues used trapped-ion qubits inside optical cavities. For each qubit, they excited the ion using a dual-wavelength laser, prompting the ion to emit a single photon. The photon’s polarization depended on which of the two laser wavelengths the ion absorbed, entangling the photon with the ion’s final state. To entangle the two ions, the team then transmitted the photon from one ion through 510 m of optical fiber to a beam splitter near the other ion, where the two photons interacted. The researchers claimed successful entanglement when they subsequently detected a pair of photons with specific individual polarizations.

Artificial intelligence (AI) tools have achieved promising results on numerous tasks and could soon assist professionals in various settings. In recent years, computer scientists have been exploring the potential of these tools for detecting signs of different physical and psychiatric conditions.

Depression is one of the most widespread psychiatric disorders, affecting approximately 9.5% of American adults every year. Tools that can automatically detect signs of depression might help to reduce suicide rates, as they would allow doctors to promptly identify people in need of psychological support.

Researchers at Jinhua Advanced Research Institute and Harbin University of Science and Technology have recently developed a deep learning algorithm that could detect depression from a person’s speech. This model, introduced in a paper published in Mobile Networks and Applications, was trained to recognize emotions in by analyzing different relevant features.

Quantum sensing represents one of the most promising applications of quantum technologies, with the aim of using quantum resources to improve measurement sensitivity. In particular, sensing of optical phases is one of the most investigated problems, considered key to developing mass-produced technological devices.

Optimal usage of quantum sensors requires regular characterization and calibration. In general, such calibration is an extremely complex and resource-intensive task—especially when considering systems for estimating multiple parameters, due to the sheer volume of required measurements as well as the computational time needed to analyze those measurements. Machine-learning algorithms present a powerful tool to address that complexity. The discovery of suitable protocols for algorithm usage is vital for the development of sensors for precise quantum-enhanced measurements.

A particular type of machine-learning algorithm known as “reinforcement learning” (RL) relies on an intelligent agent guided by rewards: Depending on the rewards it receives, it learns to perform the right actions to achieve the desired optimization. The first experimental realizations using RL algorithms for the optimization of quantum problems have been reported only very recently. Most of them still rely on prior knowledge of the model describing the system. What is desirable is instead a completely model-free approach, which is possible when the agent’s reward does not depend on the explicit system model.

This strange behavior doesn’t apply only to light. If you were to get in a rocket and blast off through a rotating universe, you, too, would get caught up in the rotation. And because of that rotation, your movement would double back on itself. When you returned to your starting point, however, you would find yourself arriving before you had left.

In a manner of speaking, a rotating universe would be capable of rotating your future into your own past, allowing you to travel back in time.

An explosion of cyberattacks is infecting servers around the world with crippling ransomware by exploiting a vulnerability that was patched two years ago, it was widely reported on Monday.

The hacks exploit a flaw in ESXi, a hypervisor VMware sells to cloud hosts and other large-scale enterprises to consolidate their hardware resources. ESXi is what’s known as a bare-metal, or Type 1, hypervisor, meaning it’s essentially its own operating system that runs directly on server hardware. By contrast, servers running the more familiar Type 2 class of hypervisors, such as Oracle’s VirtualBox, run as apps on top of a host operating system. The Type 2 hypervisors then run virtual machines that host their own guest OSes, such as Windows, Linux, or, less commonly, macOS.