September 30, 2022

Theory suggests Quantum Computers should be exponentially faster in Machine Learning

Theory suggests Quantum Computers should be exponentially faster in Machine Learning

A team of researchers including Google Quantum AI has developed a theory suggesting that quantum computers should be exponentially faster on some learning tasks than classical machines. The scientists tested their work on Google’s Sycamore quantum computer.

To find out if the idea might be possible, and more importantly, if the results would be better than those achieved on classical computers, the researchers posed the problem in a novel way: they devised a machine learning task that would learn via experiments repeated many times over. They then developed theories describing how a quantum system could be used to conduct such experiments and to learn from them. They found that they were able to prove that a quantum computer could do it, and that it could do it much better than a classical system. In fact, they found a reduction in the required number of experiments needed to learn a concept to be four orders of magnitude lower than for classical systems. The researchers then built such a system and tested it on Google’s Sycamore quantum computer and confirmed their theory.

The work suggests that if a usable, real-word quantum computer is ever developed, it might be capable of leaning new things on a nearly unimaginable scale. (Phys.org)

The paper has been published in the journal Science.

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