Lifeboat Foundation

Quantum illumination uses entangled signal-idler photon pairs to boost the detection efficiency of low-reflectivity objects in environments with bright thermal noise. Its advantage is particularly evident at low signal powers, a promising feature for applications such as noninvasive biomedical scanning or low-power short-range radar. Here, we experimentally investigate the concept of quantum illumination at microwave frequencies. We generate entangled fields to illuminate a room-temperature object at a distance of 1 m in a free-space detection setup. We implement a digital phase-conjugate receiver based on linear quadrature measurements that outperforms a symmetric classical noise radar in the same conditions, despite the entanglement-breaking signal path. Starting from experimental data, we also simulate the case of perfect idler photon number detection, which results in a quantum advantage compared with the relative classical benchmark. Our results highlight the opportunities and challenges in the way toward a first room-temperature application of microwave quantum circuits.

Quantum sensing is well developed for photonic applications (1) in line with other advanced areas of quantum information (2–5). Quantum optics has been, so far, the most natural and convenient setting for implementing the majority of protocols in quantum communication, cryptography, and metrology (6). The situation is different at longer wavelengths, such as tetrahertz or microwaves, for which the current variety of quantum technologies is more limited and confined to cryogenic environments. With the exception of superconducting quantum processing (7), no microwave quanta are typically used for applications such as sensing and communication. For these tasks, high-energy and low-loss optical and telecom frequency signals represent the first choice and form the communication backbone in the future vision of a hybrid quantum internet (8–10).

Despite this general picture, there are applications of quantum sensing that are naturally embedded in the microwave regime. This is exactly the case with quantum illumination (QI) (11–17) for its remarkable robustness to background noise, which, at room temperature, amounts to ∼10³ thermal quanta per mode at a few gigahertz. In QI, the aim is to detect a low-reflectivity object in the presence of very bright thermal noise. This is accomplished by probing the target with less than one entangled photon per mode, in a stealthy noninvasive fashion, which is impossible to reproduce with classical means. In the Gaussian QI protocol (12), the light is prepared in a two-mode squeezed vacuum state with the signal mode sent to probe the target, while the idler mode is kept at the receiver.

Instead of shutting down, Sweden opted for much milder measures to stop the spread of the coronavirus. The idea looked appealing, with the possibility of containing the pandemic at a much lower economic cost. So far, however, the statistics suggest the Swedish model is more disaster than panacea.

Army researchers predict quantum computer circuits that will no longer need extremely cold temperatures to function could become a reality after about a decade.

For years, solid-state quantum technology that operates at room temperature seemed remote. While the application of transparent crystals with optical nonlinearities had emerged as the most likely route to this milestone, the plausibility of such a system always remained in question.

Now, Army scientists have officially confirmed the validity of this approach. Dr. Kurt Jacobs, of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, working alongside Dr. Mikkel Heuck and Prof. Dirk Englund, of the Massachusetts Institute of Technology, became the first to demonstrate the feasibility of a quantum logic gate comprised of photonic circuits and optical crystals.

Vesselin Dimitrov’s proof of the Schinzel-Zassenhaus conjecture quantifies the way special values of polynomials push each other apart.

NASA and the makers of “Kerbal Space Program 2” are looking for gamers with the right stuff to recreate SpaceX’s Demo-2 Crew Dragon mission.

Brain injuries can vary greatly in their severity, but assessing the extent of the damage is far from a simple undertaking. Scientists in the UK have developed a new AI algorithm that could help narrow the margin for error, with the ability to detect and categorize different types of brain lesions to gauge the impact of an injury.

One of the tools doctors use to assess brain injuries is a CT scan, which can reveal signs of damage, such as lesions, on the brain. But analyzing these scans to reach a diagnosis is a time-consuming process for radiologists, and given the complex nature of the organ, it can see tell-tale signs often overlooked.

“CT is an incredibly important diagnostic tool, but it’s rarely used quantitatively,” said Professor David Menon, from the University of Cambridge and senior author of the new study. “Often, much of the rich information available in a CT scan is missed, and as researchers, we know that the type, volume and location of a lesion on the brain are important to patient outcomes.”

“A wealth of information creates a poverty of attention,” said Andy McMurray, co-founder and CIO of Medal, an AI-based software company developing tools for health care, during an interview with Healthcare IT News.

This is especially true in the operating room, where surgery teams at the University of Iowa Hospital have reduced surgical site infection by 74% using DASH Analytics’ high-definition care platform (HDCP). This system observes data from the operation in real time and compares it to a patient’s history and its own infection models. Toward the end of the procedure, it automatically provides the surgeon with recommendations to reduce infection during wound closure. Furthermore, it notes whether the surgeon follows its suggestions or not and compares that to the outcome of the patient. This information is then used to both improve its infection model and improve the surgeon’s own performance in future surgeries.

Advances in AI build off of each other. One breakthrough opens the doors for more possibilities in the future, which in turn leads to even more breakthroughs at an exponential rate. Just as the Industrial Revolution automated back-breaking physical labor, the AI Revolution is poised to automate mind-numbing mental labor. Based on what we’ve seen in the last 10 years alone, we can expect to see this boom very soon.

A source familiar with the matter told Electrek that Tesla has chosen Austin, Texas for its next factory and it’s going to happen quickly.

The race to secure Tesla’s next factory is apparently over.

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Rice University engineers adapt 2D ‘sandwich’ for surface-enhanced Raman spectroscopy.

A sandwich of molybdenum, sulfur, and selenium turns out to be deliciously useful for detecting biomolecules.

Tests at Rice University’s Brown School of Engineering of a two-dimensional Janus compound showed it could be an effective and universal platform for improving the detection of biomolecules via surface-enhanced Raman spectroscopy (SERS).