A new quantum algorithm speeds by solving a large class of problems

The original version BY this story appeared in Magazine.

For computers scientists, problem solving is a little as mountaineering. First they have to solve a problem to solve – becoming identifying a roof to climb – and then they need to develop a strategy to solve it. Classic and quantum researchers compete using different strategies, with a healthy rivalry between the two. Quantum researchers report a quick way to solve a problem – often by scaling a roof that no one thought is worth climbing – then classic teams compete to see if they can find a better way.

This competition almost always ends up as a virtual tie: when researchers think they have designed a quantum algorithm that works faster or better than anything else, classical researchers usually come up with what is equal to it. Just last week, an alleged quantum speed, published in the journal Sciencemet with immediate skepticism by two separate groups showing how to perform similar calculation in classic cars.

But in a paper posted on the Arxiv.org early scientific site last year, researchers described what it looks like a quantum speed that is both persuasive and useful. Researchers describe a new quantum algorithm that works faster than all those classic known in finding good solutions for a wide class of optimism problems (which require the best possible solution between a large number of choices).

So far, no classic algorithm has destroyed the new algorithm, known as the decipher quantum interferometry (DQI). “Is” a progress in quantum algorithms, “said Collegea mathematician at Reichman University and a prominent skeptic of quantum computing. Quantum algorithms reports excite researchers, partly because they can illuminate new ideas for difficult problems, and partly because, for all the movements around quantum cars, it is not clear what problems will actually benefit from them. A quantum algorithm that exceeds all the classical ones known in the tasks of optimism would represent a major step forward in utilizing the potential of quantum computers.

“I’m enthusiastic about it,” said Ronald de wolfA CWI theoretical scientist at the National Institute for Mathematics and Computer Science in the Netherlands, which was not included in the new algorithm. But at the same time, he warned that it is still quite possible that researchers will eventually find a classic algorithm that does just as well. And due to the lack of quantum equipment, it will still be a while before they can test the new algorithm empirically.

The algorithm can inspire new jobs on the classic side, according to Ewin tangA computer scientist at the University of California, Berkeley, who came to light Creation of classic algorithms that match quantum ones. The new claims “are quite interesting that I will tell classic people-algorithms,” hey, you have to look at this work and work for this problem, “she said.

The best way forward?

When competing classic and quantum algorithms, they often do so in the battlefield of optimism, a field focused on finding the best options for solving a prickly problem. Researchers usually focus on the problems in which the number of possible solutions erupts while the problem becomes higher. What is the best way for a delivery truck to visit 10 cities in three days? How should you pack the parcels in the back? Classic methods of solving these problems, which often include igniting possible solutions in smart ways, become quickly unaffordable.

The specific problem of optimism that DQI treats is approximately this: you are given a collection of points on a sheet of paper. You have to come up with a mathematical function that goes through these points. Specifically, your function should be a polynomial-a combination of variables raised for integers exponents and multiplied by coefficients. But it cannot be too complicated, meaning that the powers cannot become too high. This gives you a curved line that shakes up and down as it moves through the cheek. Your task is to find the Wiggly line that affects the most points.

Changes of this problem occur in various forms throughout the science of computers, especially in the coding of errors and cryptography – fields focused on secure and accurate coding data as transmitted. The DQI researchers knew, in essence, that plotting a better line is similar to shifting a loud coded message closer to its correct meaning.