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What does quantum computing have in common with the Oscar-winning movie “Everything Everywhere All at Once”? One is a mind-blowing work of fiction, while the other is an emerging frontier in computer science — but both of them deal with rearrangements of particles in superposition that don’t match our usual view of reality.

Fortunately, theoretical physicist Michio Kaku has provided a guidebook to the real-life frontier, titled “Quantum Supremacy: How the Quantum Computer Revolution Will Change Everything.”

“We’re talking about the next generation of computers that are going to replace digital computers,” Kaku says in the latest episode of the Fiction Science podcast. “Today, for example, we don’t use the abacus anymore in Asia. … In the future, we’ll view digital computers like we view the abacus: old-fashioned, obsolete. This is for the garbage can. That’s how the future is going to evolve.”

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More than one in three new vehicles sold in 2030 will be electric thanks to “explosive” growth in the market, according to the International Energy Agency (IEA).

The influential Paris-based group says electric cars are already on track to make up 18% of sales in 2023. With new policies driving growth in the US and the EU, the share of electric models in 2030 is now set to be more than double what it expected just two years ago.

The expansion means that the demand for oil-based fuels such as petrol and diesel in the road transport sector will start to decline within just two years. Around 5% of current oil demand will have been wiped out by 2030, it adds.

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According to computational complexity theory, mathematical problems have different levels of difficulty in the context of their solvability. While a classical computer can solve some problems ℗ in polynomial time—i.e., the time required for solving P is a polynomial function of the input size—it often fails to solve NP problems that scale exponentially with the problem size and thus cannot be solved in polynomial time. Classical computers based on semiconductor devices are, therefore, inadequate for solving sufficiently large NP problems.

In this regard, quantum computers are considered promising as they can perform a large number of operations in parallel. This, in turn, speeds up the NP problem-solving process. However, many physical implementations are highly sensitive to thermal fluctuations. As a result, quantum computers often demand stringent experimental conditions such extremely low temperatures for their implementation, making their fabrication complicated and expensive.

Fortunately, there is a lesser-known and as-yet underexplored alternative to quantum computing, known as probabilistic computing. Probabilistic computing utilizes what are called “stochastic nanodevices,” whose operations rely on thermal fluctuations, to solve NP problems efficiently. Unlike in the case of quantum computers, thermal fluctuations facilitate problem solving in probabilistic computing. As a result, probabilistic computing is, in fact, easier to implement in real life.

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Hollywood Park police also suggested consumers use pumps that are closer to the store or that have video surveillance as thieves typically target pumps that conceal them while they switch out their device.

Colby also told KENS 5 consumers should use the tap-to-pay option when possible.

“Really the best thing you can do is use tap-to-pay because you are not exposing your magnetic stripe. They’re going after the information on the mag stripe. It you don’t swipe the card it’s your safest bet,” Colby said.

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Maybe you can’t tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what’s going on inside simply by observing properties of the material’s surface.

The team used a type of machine learning known as to compare a large set of simulated data about materials’ external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the data.

The results have been published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.

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