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Category: computing

More insights on a more controlled Quantum.

By a News Reporter-Staff News Editor at Physics Week — New research on Physics Research is the subject of a report. According to news reporting from Tokyo, Japan, by VerticalNews editors, the research stated, “A computational method called the local-field response method is proposed, where spins evolve by responding to an effective field consisting of gradually decreasing external fields and spin-spin interactions, similarly to what is carried out in adiabatic quantum computing (AQC). This method is partly quantum-mechanical.”

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“With the operational function that we have proposed in these memory cells, there will be no need for time-consuming magnetization and demagnetization processes. This means that read and write operations will take only a few hundred picoseconds, depending on the materials and the geometry of the particular system, while conventional methods take hundreds or thousands of times longer than this,” said the study author Alexander Golubov, the head of Moscow Institute of Physics and Technology (MIPT)’s Laboratory of Quantum Topological Phenomena in Superconducting Systems.

Golubov and colleagues at Moscow State University have proposed creating basic memory cells based on quantum effects in superconductor “sandwiches.” Superconductors were predicted in the 1960s by the British physicist Brian Josephson. The electrons in these “sandwiches,” called “Josephson junctions,” are able to tunnel from one layer of a superconductor to another, passing through the dielectric like balls passing through a perforated wall.

Today, Josephson junctions are used both in quantum devices and conventional devices. For example, superconducting qubits are used to build the D-wave quantum system, which is capable of finding the minima of complex functions using the quantum annealing algorithm. There are also ultra-fast analogue-to-digital converters, devices to detect consecutive events, and other systems that do not require fast access to large amounts of memory. There have also been attempts to use the Josephson Effect to create ordinary processors. An experimental processor of this type was created in Japan in the late 1980s. In 2014, the research agency IAPRA resumed its attempts to create a prototype of a superconducting computer.

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Specifically, artificially intelligent computers…

As sophisticated algorithms can complete tasks we once thought impossible, computers are seeming to become a real threat to humanity. Whether they decide to pulp us into human meat paste, or simply make our work completely unnecessary, argues technology reporter Alex Hern, we should be afraid of computers.

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Even if we don’t create a true AI for a thousand years, these algorithms, pared with our exponentially increasing computing power, could have much of the same effect on our civilization as the more traditional, AI-centric type Singularity. Very, very soon.

A schematic diagram of machine learning for materials discovery (credit: Chiho Kim, Ramprasad Lab, UConn)

Replacing inefficient experimentation, UConn researchers have used machine learning to systematically scan millions of theoretical compounds for qualities that would make better materials for solar cells, fibers, and computer chips.

Led by UConn materials scientist Ramamurthy ‘Rampi’ Ramprasad, the researchers set out to determine which polymer atomic configurations make a given polymer a good electrical conductor or insulator, for example.

A polymer is a large molecule made of many repeating building blocks. The most familiar example is plastics. What controls a polymer’s properties is mainly how the atoms in the polymer connect to each other. Polymers can also have diverse electronic properties. For example, they can be very good insulators or good conductors. And what controls all these properties is mainly how the atoms in the polymer connect to each other.

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You may have never heard of AMD, but you’ve almost certainly used products powered by the company’s technologies.

AMD, or Advanced Micro Devices, is one of the biggest chipmakers in the world. The 46-year-old California company makes computer chips and all the related tech needed to power applications on PCs, smartphones, tablets, and more.

On Monday, AMD surprised everyone with its newest initiative: The Sulon Q, built out of a partnership with Ontario-based Sulon Technologies.

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Maybe someone saw the article on the team in Australia who solved this issue last month; glad folks are collaborating more in this space because we all win when we do.

One of the obstacles that have kept quantum computers on the distant horizon is the fact that quantum bits — the building blocks with which they’re made — are prone to magnetic disturbances. Such “noise” can interfere with the work qubits do, but on Wednesday, scientists announced a new discovery that could help solve the problem.

Specifically, by tapping the same principle that allows atomic clocks to stay accurate, researchers at Florida State University’s National High Magnetic Field Laboratory (MagLab) have found a way to give qubits the equivalent of a pair of noise-canceling headphones.

The approach relies on what are known as atomic clock transitions. Working with carefully designed tungsten oxide molecules that contained a single magnetic holmium ion, the MagLab team was able to keep a holmium qubit working coherently for 8.4 microseconds -– potentially long enough for it to perform useful computational tasks.

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Chinese scientists have developed a nano-sized electric generator that can disappear without a trace inside the human body over time, a breakthrough they claim will bring biodegradable implants on microchips closer to reality.

The technology, reported on the latest issue of Science Advances journal, will have a wide range of applications as it can generate electric pulses to repair damaged neurons and power “brain chip” implants for soldiers in the future, pundits said.

At present, most implants must be surgically removed at the end of their lifespan. To address this issue, a number of small electric devices made from biodegradable materials that can absorbed by the human body after use have been developed around the world.

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https://youtube.com/watch?v=hX0UELNRR1I

A few weeks ago DJI unveiled its newest drone, the Phantom 4, the first craft to offer robust obstacle avoidance at a price the average consumer can afford. It relied on computer vision to power its autonomous flight, and since DJI had shown off this kind of tech before, we assumed that all the hardware on the Phantom 4 was homegrown, or backed by a giant like Intel. But today the chipmaker Movidius announced that its latest offer, the Myriad 2, was at the center of the onboard processor powering the Phantom 4’s incredible new abilities.

As it turns out this isn’t the first time Movidius has partnered with a big name to develop cutting edge technology. Back in 2014 its first chip, the Myriad 1, was revealed as the brains inside of Google’s first generation of Project Tango tablets. After a decade toiling in relative obscurity, the small 125 person company is suddenly poised to emerge as a leader at the intersection of several major markets — from drones to phones to virtual reality — which are looking for ways to enable cheap, power-efficient computer vision.

“The company was founded in late 2005, so we’ve had a long gestation,” says CEO Remi El-Ouazzane with a laugh. In its early years it found some business converting old movies into 3D, helping to shore up content offerings for the 3D TV market that never took off. In 2010 its chips were put to use as an engine for 3D rendering, but it was competing with plenty of established chip makers in that market. It wasn’t until 2013, and its partnership with Tango, that the company realized how widespread the application of computer vision could be, and focused in on optimizing for what it believed would be the next wave of devices.

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For those who read here often, there are clear signs that the FPGA is set to become a compelling acceleration story over the next few years.

From the relatively recent Intel acquisition of Altera by chip giant Intel, to less talked-about advancements on the programming front (OpenCL progress, advancements in both hardware and software from FPGA competitor to Intel/Altera, Xilinx) and of course, consistent competition for the compute acceleration market from GPUs, which dominate the coprocessor market for now.

Last week at the Open Compute Summit we finally got a glimpse of one of the many ways FPGAs might fit into the hyperscale ecosystem (along with other future hardware insight) with an announcement that Intel will be working on future OCP designs featuring an integrated FPGA and Xeon chip. Unlike what many expected, the CPU mate will not be a Xeon D, but rather a proper Broadwell EP. As seen below, this appears to be a 15-core part (Intel did not confirm, but their diagram makes counting rather easy) matched with the Altera Arria 10 GX FPGAs.

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LONDON & MIAMI–()–Blue Prism, the pioneering developer of enterprise Robotic Process Automation (RPA) software, today announced its debut on AIM of the London Stock Exchange (LSE). The first developer of software robots to trade on the public markets, Blue Prism, working closely with its global network of partners, grew 35% last year and has deployments with more than 74 customers, including a number of the world’s largest banks, insurers, utilities, healthcare, telecommunications, service providers and other regulated industries. The initial public offering (IPO) will allow Blue Prism to support its global growth plans and enhance its profile within the RPA marketplace.

“Today’s milestone follows a successful year for the company, and marks a shift in acceptance for software robots as a mainstream choice for the enterprise digital workforce,” said Alastair Bathgate, co-founder and CEO of Blue Prism. “Software robots have been deployed successfully and strategically by large, blue chip organizations that have derived tremendous value from this new solution to the labor market, it’s not science fiction.”

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