The original version of This story appeared in How many magazine.
For computer scientists, problem solving is a bit like mountaineering. They must first choose a problem to be resolved – to identify a peak to climb – and they must then 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 expanding a peak that no one thought of climbing – while conventional teams run to see if they can find a better way.
This competition almost always ends as a virtual link: when researchers think they have designed a quantum algorithm that works faster or better than anything else, classic researchers generally find one that equivals it. Last week, an alleged quantum acceleration, published in the review Sciencemet with immediate skepticism of two distinct groups that have shown how to play similar calculations on classic machines.
But in an article published on the scientific preparation site Arxiv.org last year, the researchers described what looks like quantum acceleration which is both convincing and useful. Researchers have described a new quantum algorithm which works faster than all the classics known to find good solutions to a large class of optimization problems (which are looking for the best possible solution among a huge number of choices).
Until now, no classic algorithm has dethroned the new algorithm, known as the quantum interferometry decoded (DQI). It is “a breakthrough in quantum algorithms,” said Collegemathematician at Reichman University and a prominent skeptic of quantum computer science. Quantum algorithms' reports excite researchers, in part because they can shed light on new ideas on difficult problems, and in part because, for the whole buzz around quantum machines, it is not clear what problems will really benefit from it. A quantum algorithm that surpasses all the conventional tasks known on optimization tasks would represent a major step in the harness of the potential of quantum computers.
“I am enthusiastic about it,” said Ronald de WolfA theoretical computer scientist at CWI, the National Research Institute for Mathematics and Computer Science in the Netherlands, which was not involved in the new algorithm. But at the same time, he warned that it is always possible that researchers possibly find a classic algorithm which does it just as well. And due to the lack of quantum material, it will always be a certain time before being able to empirically test the new algorithm.
Algorithm could inspire new work Ewin TangA computer scientist from the University of California in Berkeley, who has taken importance in adolescence by Creation of conventional algorithms that correspond to those quantum. The new claims “are interesting enough to tell people of classic algorithms:” Hey, you should watch this document and work on this problem, “she said.
The best way to follow?
When conventional and quantum algorithms compete, they often do it on the battlefield of optimization, a field focused on the search for the best options to solve a thorny problem. Researchers generally focus on the problems in which the number of possible solutions explodes as the problem increases. What is the best way for a delivery truck to visit 10 cities in three days? How should you pack the packages at the back? Classic methods of solving these problems, which often involve disseminating possible solutions, quickly become untenable.
The specific optimization problem that DQI tackles about this: you have a collection of points on a sheet of paper. You must find a mathematical function that goes through these points. More specifically, your function must be a polynomial – a combination of variables raised on exhibitors of whole number and multiplied by coefficients. But that cannot be too complicated, which means that powers cannot become too high. This gives you a curved line that goes up from top to bottom as it moves through the page. Your work is to find the Wiggly line that affects the most points.
The variations in this problem appear in various forms through computer science, in particular in the coding of errors and cryptography – fields focused on coding data in complete safety and precisely during their transmission. DQI researchers recognized, fundamentally, that the tracing of a better line is like moving a coded message noisy closer to its precise meaning.
