(Physics.aps.org) This interview is with Maria Schuld, a physicist who works for the Canadian quantum computing company Xanadu from her home in South Africa.
Schuld discussed quantum machine-learning models where the thing that’s used to solve the task is a quantum computation. These computations don’t have clear recipes to follow, like Shor’s algorithm—a quantum algorithm for integer factorization. Rather, they are more an abstract skeleton that the model uses to train itself.
She isinterestedin quantum machine-learning models that use so-called kernel methods. Classical kernel methods are a class of algorithm used for pattern analysis, and they were very popular in the 1990s. The mathematics of quantum computing looks very similar to classical kernel methods. This similarity allows us to apply results from classical methods to quantum computing.
She forecast what is next for the field. “For me, it’s better theory. We are very far from being able to do meaningful experiments. To recognize a typical image today would require millions of quantum gates, yet the best experiments have just a handful of gates. With theory, we can build models to answer how quantum machine-learning algorithms might work and what improvements they might show. Then, when the machines are ready, we can start testing the answers.”