Original version of This story Appears in Quanta Magazine.
For computer scientists, solving problems is a bit like climbing. First, they need to choose a problem to solve – Akin to identify the peak to climb – and they need to develop a strategy to solve it. Classical and quantum researchers compete using a variety of strategies and develop healthy rivalries between the two. Quantum researchers report rapid methods to solve problems. Often, by expanding the peaks that no one thinks are worth climbing, we see if classic teams can compete and find a better way.
This contest will mostly end as a virtual tie. When you think of researchers as devising quantum algorithms that work faster or better than anything else, classical researchers usually come up with something equal to that. It is said to be the Quantum Speeepup that was published in the journal last week. Sciencemet with immediate skepticism from two separate groups that demonstrated how to do it. resemble Calculation On a classic machine.
However, last year, in a paper published on the scientific prelint site arxiv.org, researchers explained what it looks like. Convincing and useful quantum speedup. Researchers have described new quantum algorithms that work faster than all known classical algorithms when finding good solutions to a wide range of classes of optimization problems (looking for the best possible solution out of a vast number of options).
So far, there is no classical algorithm that abandons a new algorithm known as decoded quantum interferometry (DQI). He said it was a “breakthrough in quantum algorithms.” Gil Karaiwith a mathematician at Reichmann University. A prominent skeptic of quantum computing. Quantum algorithm reports excite researchers as they can illuminate new ideas on difficult problems. Also, it is not clear which problems will actually benefit from all the topics around quantum machines. Quantum algorithms outweigh all known classics in optimization tasks represent a major advance in exploiting the possibilities of quantum computers.
“I’m eager about that,” he said. Ronald de Wolftheoretical computer scientist at CWI, a Dutch national laboratory of mathematics and computer science who was not involved in the new algorithm. But at the same time, he warned that there is a very high chance that researchers will ultimately find classic algorithms that they do as well. Also, due to the lack of quantum hardware, it will still take some time before we can empirically test new algorithms.
Algorithms may inspire new work on classical aspects, Ewin Tanga computer scientist at the University of California, Berkeley, and became a well-known teenager. Create classical algorithms that match quantum ones. The new argument is “so interesting enough to tell people that classical algorithms are, ‘Hey, we should look at this paper and tackle this issue,” she said.
What’s the best way to do it in the future?
When classical and quantum algorithms compete, they often do so on the battlefield of optimization. This is an area focused on finding the best options to solve troublesome problems. Researchers usually focus on problems where the number of possible solutions explodes as the problem grows. What is the best way for a delivery truck to visit 10 cities in 3 days? How should I pack the parcel in the back? Classic methods of solving these problems often involve running through possible solutions in clever ways, and are quickly unacceptable.
The specific optimization problems that DQI tackles are roughly misaligned. A collection of points is given on paper. You need to come up with the mathematical functions that pass through these points. Specifically, the function must be polynomial. This is a combination of variables that are raised to the full-text index and multiplied by the coefficient. But that’s not too complicated. In other words, you cannot be too powerful. This gives you a curve that wiggles up and down as you move across the page. Your job is to find the wavy lines that touch the most points.
Variations of this issue are presented in a variety of ways, particularly in error coding and encryption. DQI researchers have recognized that essentially plotting a better line is similar to shifting a noisy, encoded message that is close to its exact meaning.
