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China Launches World’s Fastest Quantum Computers | China’s Advancement In Quantum Computers #technology.

“Techno Jungles”

In 2019, Google announced that its 53-qubit Sycamore processor had finished a task in 3.3 minutes that would have taken a conventional supercomputer at least 2.5 days to accomplish. According to reports, China’s 66-Qubit Zuchongzhi 2 Quantum Processor was able to complete the same task 1 million times faster in October of last year. Together with the Shanghai Institute of Technical Physics and the Shanghai Institute of Microsystem and Information Technology, a group of researchers from the Chinese Academy of Sciences Center for Excellence in Quantum Information and Quantum Physics were responsible for the development of that processor.

According to NDTV, the Chinese government under Xi Jinping has spent $10 billion on the country’s National Laboratory for Quantum Information Sciences. This demonstrates China’s significant commitment to the field of quantum computing. According to Live Science, the nation is also a world leader in the field of quantum networking, which involves the transmission of data that has been encoded through the use of quantum mechanics over great distances.

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Companies and research labs across the globe are working towards getting their nascent quantum technologies out of the lab and into the real world, with the US technology giant IBM being a key player. In May this year, IBM Quantum unveiled its latest roadmap for the future of quantum computing in the coming decade, and the firm has set some ambitious targets. Having announced its Eagle processor with 127 quantum bits (qubits) last year, the company is now developing the 433-qubit Osprey processor for a debut later this year, to be followed in 2023 by the 1121-qubit Condor.

But beyond that, the company says, the game will switch to assembling such processors into modular circuits, in which the chips are wired together via sparser quantum or classical interconnections. That effort will culminate in what they refer to as their 4158-qubit Kookaburra device in 2025. Beyond then, IBM forecasts modular processors with 100,000 or more qubits, capable of computing without the errors that currently make quantum computing a matter of finding workarounds for the noisiness of the qubits. With this approach, the company’s quantum computing team is confident that it can achieve a general “quantum advantage”, where quantum computers will consistently outperform classical computers and conduct complex computations beyond the means of classical devices.

While he was in London on his way to the 28 th Solvay conference in Brussels, which tackled quantum information, Physics World caught up with physicist Jay Gambetta, vice-president of IBM Quantum. Having spearheaded much of the company’s advances over the past two decades, Gambetta explained how these goals might be reached and what they will entail for the future of quantum computing.

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An information engine uses information to convert heat into useful energy. Such an engine can be made, for example, from a heavy bead in an optical trap. A bead engine operates using thermal noise. When noise fluctuations raise the bead vertically, the trap is also lifted. This change increases the average height of the bead, and the engine produces energy. No work is done to cause this change; rather, the potential energy is extracted from information. However, measurement noise—whose origin is intrinsic to the system probing the bead’s position—can degrade the engine’s efficiency, as it can add uncertainty to the measurement, which can lead to incorrect feedback decisions by the algorithm that operates the engine. Now Tushar Saha and colleagues at Simon Fraser University in Canada have developed an algorithm that doesn’t suffer from these errors, allowing for efficient operation of an information engine even when there is high measurement noise [1].

To date, most information engines have operated using feedback algorithms that consider only the most recent bead-position observation. In such a system, when the engine’s signal-to-noise ratio falls below a certain value, the engine stops working.

To overcome this problem, Saha and colleagues instead use a “filtering” algorithm that replaces the most recent bead measurement with a so-called Bayesian estimate. This estimate accounts for both measurement noise and delay in the device’s feedback.

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In a first-of-its-kind test for planetary defense, NASA’s DART spacecraft is scheduled next week to crash into an asteroid and alter the celestial body’s course.

If all goes according to plan, on September 26th at 7:14 pm Eastern Daylight Time, NASA’s DART spacecraft will meet a fiery end. DART, whose name stands for Double Asteroid Redirection Test, is poised to intentionally crash into an asteroid that, at the time of impact, will be 11 million km from Earth. The goal of the mission is to alter the speed and trajectory of the impacted space boulder. The technology developed for the mission could one day aid in shifting the orbit of an asteroid that—unlike this one—is on a collision course with Earth.

“Our DART spacecraft is going to impact an asteroid in humanity’s first attempt to change the motion of a natural celestial body,” said Tom Statler, a scientist in NASA’s planetary defense team, in a recent press conference about the mission. “It will be a truly historic moment for the entire world.”

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Experiments demonstrate that biological cells actively change shape to respond to their surroundings when moving in confined regions.

The movement of cells is essential for embryo development and wound healing. A study of individual human cells moving on a micropatterned surface reveals some of the basic principles governing this movement and shows how cells adapt their shape and behavior to the geometry of their surroundings [1]. The researchers developed a theoretical model, based on their experimental findings, that could be used to study and predict cell movement in more complex environments.

The shapes of animal cells are controlled in part by a web of protein filaments called the cytoskeleton, which can be rearranged by the cell to drive motion. For example, a cell can begin moving by creating a protrusion that bulges out from its surface. Such movement depends on the cell’s adhesion to the surrounding surfaces and on the formation of an asymmetrical arrangement of the cytoskeleton, referred to by biologists as polarity, which drives the growth of protrusions. The motion is also affected by the internal structures of the cell, especially the nucleus, which is less compressible than the fluid cytoplasm.

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Modern medicine forces bacteria to adapt: in response to antibiotic treatment, they either increase their fitness or die out. Whether a bacterial population survives or not depends on a combination of its genetics and environment—the antibiotic concentration—at a given moment. Now Suman Das of the University of Cologne, Germany, and colleagues simulate the effect on adaptation of an environment that is constantly changing [1]. Using a model that describes how slow-moving disordered systems respond to external forces, the researchers find that microbe evolution in changing drug concentrations exhibits hysteresis and memory formation. They use analytical methods and numerical simulations to connect these statistical physics concepts to bacterial drug resistance.

The team’s model examines changes in a bacterial population’s genetic sequences. By combining data on bacterial growth rates with statistical tools, the researchers describe how the bacterial genome can store information about both present and past drug concentrations. Their simulations start with a genetic sequence optimized for a certain antibiotic concentration. They then track how the sequence mutates when the concentration shifts to another value. When the concentration increases and then reduces to a lower value, the genetic route taken on the downward path depends on the changes on the upward path. How different the mutation routes are depends on the rate of concentration change.

The researchers find that this behavior mimics that of disordered systems driven by external forces, such as ferromagnetic materials subjected to magnetic fields or amorphous materials subjected to a shearing force. They say that while their approach focuses on the evolution of drug resistance, the framework can be adapted to other problems in evolutionary biology that involve changing environmental parameters.

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