But , as Hopkins explains , even small improvements to those algorithms will have a sizeable impact , with evidence already demonstrating that quantum computers can help to resolve these common problems .
“ We ’ ve recently published a paper where we took information about real debit and credit card details and transactions , and passed them through a quantum algorithm and two conventional algorithms , XG Boost and Random Forest ,” Hopkins says .
“ Even with today ’ s quantum hardware , providing we let the quantum computer select the parameters to predict the fraud , then we would get the same level of accuracy out of an intermediate-scale quantum computer ,” he explains . “ That , in itself , is not bad going , but it doesn ’ t get you to that quantum advantage .”
However , as Hopkins describes , the quantum algorithm was able to make qualitatively different judgements and , as a result , come to different conclusions .
“ When we look at these results more closely , we found that , first of all , the quantum algorithm chose different parameters ,” he says . “ And then , when we looked at the results again , we saw that it was making qualitatively different judgments . The accuracy was the same , but it was making a judgement on different elements and coming to different conclusions in many cases .”
As Hopkins describes , these hybrid applications will have a number of use cases , particularly in the world of ML .
“ I think what you ’ re going to see , especially in the ML space , is that these hybrid algorithms will emerge fairly early on , where you are combining the power of an existing algorithm that can run at high speed with a quantum algorithm , which will run slower , but will actually take a qualitative different decision , using a completely different algorithm than the other one ,” he says .