Lifeboat Foundation

So what would the furniture of the future be like? some future furniture.

Once you have your own place, you want to make it truly yours by choosing the decoration and furniture. Even if you aren’t, you can’t miss these smart pieces of furniture. Some are surprising, others seem completely SciFi. Check out this review of smart furniture and let us know what you think!

Continue reading “Smart Furniture | Ingenious Space Saving Designs And Hidden Doors” »

Widespread human SARS-CoV-2 infections combined with human-wildlife interactions create the potential for reverse zoonosis from humans to wildlife. We targeted white-tailed deer (Odocoileus virginianus) for serosurveillance based on evidence these deer have ACE2 receptors with high affinity for SARS-CoV-2, are permissive to infection, exhibit sustained viral shedding, can transmit to conspecifics, and can be abundant near urban centers. We evaluated 624 pre-and post-pandemic serum samples from wild deer from four U.S. states for SARS-CoV-2 exposure. Antibodies were detected in 152 samples (40%) from 2,021 using a surrogate virus neutralization test. A subset of samples was tested using a SARS-CoV-2 virus neutralization test with high concordance between tests. These data suggest white-tailed deer in the populations assessed have been exposed to SARS-CoV-2.

One-Sentence Summary Antibodies to SARS-CoV-2 were detected in 40% of wild white-tailed deer sampled from four U.S. states in 2021.

SARS-CoV-2, the virus that causes COVID-19 in humans, can infect multiple domestic and wild animal species (1 – 7). Thus, the possibility exists for the emergence of new animal reservoirs of SARS-CoV-2, each with unique potential to maintain, disseminate, and drive novel evolution of this virus. Of particular concern are wildlife species that are both abundant and live in close association with human populations (5).

As reported in a new article in Nature Reviews Physics, instead of waiting for fully mature quantum computers to emerge, Los Alamos National Laboratory and other leading institutions have developed hybrid classical/quantum algorithms to extract the most performance—and potentially quantum advantage—from today’s noisy, error-prone hardware. Known as variational quantum algorithms, they use the quantum boxes to manipulate quantum systems while shifting much of the work load to classical computers to let them do what they currently do best: solve optimization problems.

“Quantum computers have the promise to outperform classical computers for certain tasks, but on currently available quantum hardware they can’t run long algorithms. They have too much noise as they interact with environment, which corrupts the information being processed,” said Marco Cerezo, a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos and a lead author of the paper. “With variational quantum algorithms, we get the best of both worlds. We can harness the power of quantum computers for tasks that classical computers can’t do easily, then use classical computers to compliment the computational power of quantum devices.”

Current noisy, intermediate scale quantum computers have between 50 and 100 qubits, lose their “quantumness” quickly, and lack error correction, which requires more qubits. Since the late 1990s, however, theoreticians have been developing algorithms designed to run on an idealized large, error-correcting, fault tolerant quantum computer.

Quantum engineers from UNSW Sydney have removed a major obstacle that has stood in the way of quantum computers becoming a reality. They discovered a new technique they say will be capable of controlling millions of spin qubits—the basic units of information in a silicon quantum processor.

Until now, quantum computer engineers and scientists have worked with a proof-of-concept model of quantum processors by demonstrating the control of only a handful of qubits.

Continue reading “Engineers make critical advance in quantum computer design” »

Spectroscopic measurements confirm that when water is adsorbed on drops of an alkali alloy at low pressure a gold-coloured metallic layer forms as electrons rapidly move from the drop into the water.

In this work, we introduce a classical variational method for simulating QAOA, a hybrid quantum-classical approach for solving combinatorial optimizations with prospects of quantum speedup on near-term devices. We employ a self-contained approximate simulator based on NQS methods borrowed from many-body quantum physics, departing from the traditional exact simulations of this class of quantum circuits.

We successfully explore previously unreachable regions in the QAOA parameter space, owing to good performance of our method near optimal QAOA angles. Model limitations are discussed in terms of lower fidelities in quantum state reproduction away from said optimum. Because of such different area of applicability and relative low computational cost, the method is introduced as complementary to established numerical methods of classical simulation of quantum circuits.

Classical variational simulations of quantum algorithms provide a natural way to both benchmark and understand the limitations of near-future quantum hardware. On the algorithmic side, our approach can help answer a fundamentally open question in the field, namely whether QAOA can outperform classical optimization algorithms or quantum-inspired classical algorithms based on artificial neural networks^48,49,50.

Most memory resistor (“memristor”) systems use electrons as the charge carrier, but it may also be possible to use ionic carriers, similar to the way that neurons work. Robin et al. studied an aqueous electrolyte confined into a pseudo two-dimensional gap between two graphite layers (see the Perspective by Hou and Hou). The authors observed a current–voltage relation that exhibits hysteresis, and the conductance depends on the history of the system, also known as the memresistor effect. Using simulations of their system, they can model the emission of voltage spikes characteristic of neuromorphic activity.

Science, abf7923, this issue p. 687; see also abj0437, p. 628

Recent advances in nanofluidics have enabled the confinement of water down to a single molecular layer. Such monolayer electrolytes show promise in achieving bioinspired functionalities through molecular control of ion transport. However, the understanding of ion dynamics in these systems is still scarce. Here, we develop an analytical theory, backed up by molecular dynamics simulations, that predicts strongly nonlinear effects in ion transport across quasi–two-dimensional slits. We show that under an electric field, ions assemble into elongated clusters, whose slow dynamics result in hysteretic conduction. This phenomenon, known as the memristor effect, can be harnessed to build an elementary neuron. As a proof of concept, we carry out molecular simulations of two nanofluidic slits that reproduce the Hodgkin-Huxley model and observe spontaneous emission of voltage spikes characteristic of neuromorphic activity.

Wirelessly powered microchips, which have an ~1 GHz electromagnetic transcutaneous link to an external telecom hub, can be used for multichannel in vivo neural sensing, stimulation and data acquisition.