The quantum computer has once again surpassed the conventional one. But in machine learning

A team of researchers, including Google Quantum AI, has developed a new theory. According to it, quantum

computers must be exponentially fasterin some learning tasks than classical machines. In an article published in the journal Science, scientists described their theory and reported the results of testing on the Google Sycamore quantum computer. Vedran Dunko from Leiden University City published a Perspective article in the same issue of the journal, in which he outlined the idea of ​​combining quantum computing with machine learning to provide a new level of computer-based learning systems.

Machine learning is a system usingin which computers trained on data sets make educated guesses about new data. And quantum computing involves the use of subatomic particles to represent qubits as a means to perform tasks many times faster than is possible with classical computers. In a new study, scientists explored the idea of ​​running machine learning applications on quantum machines.

To find out whether this idea is feasible and thatMore importantly, whether the results would be better than on classical computers, the researchers created a machine learning task that would learn over repeated experiments. They then developed theories describing how the quantum system could be used to conduct such experiments and learn from them.

They proved that the quantum computer is not onlycopes, but also performs tasks much better than the classical system. In fact, the scientists found that the number of experiments needed to explore the concept was reduced by four orders of magnitude compared to classical systems. The researchers then built such a system and tested it on the Google Sycamore quantum computer, confirming their theory.

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