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Not only do quantum computers have the edge over classical computers on some tasks, but they are also exponentially faster, according to a new mathematical proof.
The U.K. has reported more than 300 cases.
The U.S. Centers for Disease Control and Prevention (CDC) ramped up its alert level for the ongoing monkeypox outbreak as the nation’s case count hit 30 and the global case count rose above 1,000.
The CDC now advises that travelers “practice enhanced precautions” to avoid contracting and spreading the rare viral disease, the agency’s website states (opens in new tab). The CDC says that people should avoid close contact with sick people, including those with rashes on their skin or genitals, and with dead or live wild animals, especially rodents, such as rats and squirrels, and non-human primates, meaning monkeys and apes.
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A collection of 16 qubits has been organized in such a way that they may be able to operate any computation without error. It is an important step toward constructing quantum computers that outperform standard ones.
When completing any task, a quantum computer consisting of charged atoms can detect its own faults. Because conventional computers constantly detect and rectify their own flaws, quantum computers will need to do the same in order to fully outperform them. Nevertheless, quantum effects can cause errors to propagate rapidly through the qubits, or quantum bits, that comprise these devices.
Continue reading “How Can a Quantum Computer Catch its Own Errors in Calculations?” »
Convolutional neural networks used to be the typical network architecture in modern computer vision systems. Transformers with attention mechanisms were recently presented for visual tasks, and they performed well. Convolutions and self-attention aren’t required for MLP-based (multi-layer perceptron) vision models to perform properly. As a result of these advancements, vision models are reaching new heights.
The input image is handled differently by different networks. In Euclidean space, image data is commonly represented as a regular grid of pixels. On the image, CNNs apply a sliding window and introduce shift-invariance and locality. The MLP vision transformer, which was released recently, treats the image as a series of patches.
Recognizing the items in an image is one of the most basic tasks of computer vision. The typically utilized grid or sequence structures in prior networks like ResNet and ViT are redundant and inflexible to process because the objects are usually not quadrate whose shape is irregular. An item can be thought of as a collection of parts, such as a human’s head, upper torso, arms, and legs. These sections are organically connected by joints, forming a graph structure. Furthermore, a graph is a generic data structure, with grids and sequences being special cases of graphs. Visual perception is more flexible and effective when an image is viewed as a graph.
Huge “foundation models” are turbo — charging AI progress.
They can have abilities their creators did not foresee.
Machine learning can get a boost from quantum physics.
On certain types of machine learning tasks, quantum computers have an exponential advantage over standard computation, scientists report in the June 10 Science. The researchers proved that, according to quantum math, the advantage applies when using machine learning to understand quantum systems. And the team showed that the advantage holds up in real-world tests.
“People are very excited about the potential of using quantum technology to improve our learning ability,” says theoretical physicist and computer scientist Hsin-Yuan Huang of Caltech. But it wasn’t entirely clear if machine learning could benefit from quantum physics in practice.
Solving the 3D structure at near atomic level resolution, one of the world’s hardest, giant jigsaw puzzles—the nuclear pore complex—the largest molecular machine in human cells, with structure-based AI prediction @ScienceMagazine
According the company, this innovative system enables the detection of live objects behind walls at a distance of more than 50 meters.
Camero-Tech, a member of the SK Group and an Israeli developer, producer, and marketer of pulse-based UWB micro-power radar ‘Through Wall Imaging’ systems, announced the launching of its groundbreaking XaverTM LR40 (XLR40) system, which detects live objects behind walls at distances of over 50 meters.
Single-molecule electronic devices, which use single molecules or molecular monolayers as their conductive channels, offer a new strategy to resolve the miniaturization and functionalization bottlenecks encountered by traditional semiconductor electronic devices. These devices have many inherent advantages, including adjustable electronic characteristics, ease of availability, functional diversity and so on.
To date, single-molecule devices with a variety of functions have been realized, including diodes, field-effect devices and optoelectronic devices. In addition to their important applications in the field of functional devices, single-molecule devices also provide a unique platform to explore the intrinsic properties of matters at the single-molecule level.
Regulating the electrical properties of single-molecule devices is still a key step to further advance the development of molecular electronics. To effectively adjust the molecular properties of the device, it is necessary to clarify the interactions between electron transport in single-molecule devices and external fields, such as external temperature, magnetic field, electric field, and light field. Among these fields, the use of light to adjust the electronic properties of single-molecule devices is one of the most important fields, known as “single-molecule optoelectronics.”
