Lifeboat Foundation

What’s New: In its relentless pursuit of Moore’s Law, Intel is unveiling key packaging, transistor and quantum physics breakthroughs fundamental to advancing and accelerating computing well into the next decade. At IEEE International Electron Devices Meeting (IEDM) 2021, Intel outlined its path toward more than 10x interconnect density improvement in packaging with hybrid bonding, 30% to 50% area improvement in transistor scaling, major breakthroughs in new power and memory technologies, and new concepts in physics that may one day revolutionize computing.

“At Intel, the research and innovation necessary for advancing Moore’s Law never stops. Our Components Research Group is sharing key research breakthroughs at IEDM 2021 in bringing revolutionary process and packaging technologies to meet the insatiable demand for powerful computing that our industry and society depend on. This is the result of our best scientists’ and engineers’ tireless work. They continue to be at the forefront of innovations for continuing Moore’s Law.” –Robert Chau, Intel Senior Fellow and general manager of Components Research

Why It Matters: Moore’s Law has been tracking innovations in computing that meet the demands of every technology generation from mainframes to mobile phones. This evolution is continuing today as we move into a new era of computing with unlimited data and artificial intelligence.

✅ Instagram: https://www.instagram.com/pro_robots.

You are on the PRO Robots channel and in this form we present you with high-tech news. What can Google’s army of robots really do? Can time turn backwards? Catapult rockets and a jet engine powered by plastic waste. All this and much more in one edition of high-tech news! Watch the video until the end and write your impressions about the new army of robots from Google in the comments.

Continue reading “What can Google’s army of robots do?| Physicists have turned back time | High Tech News” »

Researchers in Finland have developed a circuit that produces the high-quality microwave signals required to control quantum computers while operating at temperatures near absolute zero. This is a key step towards moving the control system closer to the quantum processor, which may make it possible to greatly increase the number of qubits in the processor.

One of the factors limiting the size of quantum computers is the mechanism used to control the qubits in quantum processors. This is normally accomplished using a series of microwave pulses, and because quantum processors operate at temperatures near absolute zero, the control pulses are normally brought into the cooled environment via broadband cables from room temperature.

As the number of qubits grows, so does the number of cables needed. This limits the potential size of a quantum processor, because the refrigerators cooling the qubits would have to become larger to accommodate more and more cables while also working harder to cool them down—ultimately a losing proposition.

While they wrestle with the immediate danger posed by hackers today, US government officials are preparing for another, longer-term threat: attackers who are collecting sensitive, encrypted data now in the hope that they’ll be able to unlock it at some point in the future.

The threat comes from quantum computers, which work very differently from the classical computers we use today. Instead of the traditional bits made of 1s and 0s, they use quantum bits that can represent different values at the same time. The complexity of quantum computers could make them much faster at certain tasks, allowing them to solve problems that remain practically impossible for modern machines—including breaking many of the encryption algorithms currently used to protect sensitive data such as personal, trade, and state secrets.

While quantum computers are still in their infancy, incredibly expensive and fraught with problems, officials say efforts to protect the country from this long-term danger need to begin right now.

These longstanding challenges are both related to how functionals behave when presented with a system that exhibits “fractional electron character.” By using a neural network to represent the functional and tailoring our training dataset to capture the fractional electron behaviour expected for the exact functional, we found that we could solve the problems of delocalization and spin symmetry-breaking. Our functional also showed itself to be highly accurate on broad, large-scale benchmarks, suggesting that this data-driven approach can capture aspects of the exact functional that have thus far been elusive.

For years, computer simulations have played a central role in modern engineering, making it possible to provide reliable answers to questions like “will this bridge stay up?” to “will this rocket make it into space?” As technology increasingly turns to the quantum scale to explore questions about materials, medicines, and catalysts, including those we’ve never seen or even imagined, deep learning shows promise to accurately simulate matter at this quantum mechanical level.

Researchers at Lawrence Berkeley National Laboratory’s Advanced Quantum Testbed (AQT) demonstrated that an experimental method known as randomized compiling (RC) can dramatically reduce error rates in quantum algorithms and lead to more accurate and stable quantum computations. No longer just a theoretical concept for quantum computing, the multidisciplinary team’s breakthrough experimental results are published in Physical Review X.

The experiments at AQT were performed on a four-qubit superconducting quantum processor. The researchers demonstrated that RC can suppress one of the most severe types of errors in quantum computers: coherent errors.

Akel Hashim, AQT researcher, involved in the experimental breakthrough and a graduate student at the University of California, Berkeley explained: “We can perform quantum computations in this era of noisy intermediate-scale quantum (NISQ) computing, but these are very noisy, prone to errors from many different sources, and don’t last very long due to the decoherence—that is, information loss—of our qubits.”

By Stina Andersson and Ellinor Wanzambi

Researchers have been working on quantum algorithms since physicists first proposed using principles of quantum physics to simulate nature decades. One important component in many quantum algorithms is quantum walks, which are the quantum equivalent of the classical Markov chain, i.e., a random walk without memory. Quantum walks are used in algorithms in areas such as searching, node ranking in networks, and element distinctness.

Consider the graph in Figure 1 and imagine that we randomly want to move between nodes A, B, C, and D in the graph. We can only move between nodes that are connected by an edge, and each edge has an associated probability that decides how likely we are to move to the connected node. This is a random walk. In this article, we are working only with Markov chains, also called the memory-less random walks, meaning that the probabilities are independent of the previous steps. For example, the probabilities of arriving at node A are the same no matter if we got there from node B or node D.

Quantum computers have the potential to solve important problems that are beyond reach even for the most powerful supercomputers, but they require an entirely new way of programming and creating algorithms.

Universities and major tech companies are spearheading research on how to develop these new algorithms. In a recent collaboration between University of Helsinki, Aalto University, University of Turku, and IBM Research Europe-Zurich, a team of researchers have developed a new method to speed up calculations on quantum computers. The results are published in the journal PRX Quantum of the American Physical Society.

“Unlike classical computers, which use bits to store ones and zeros, information is stored in the qubits of a quantum processor in the form of a quantum state, or a wavefunction,” says postdoctoral researcher Guillermo García-Pérez from the Department of Physics at the University of Helsinki, first author of the paper.