There’s a new algorithm in town – this one is quantum machine learning one and it can handle infinite dimensions. If you’re wondering what that even means, it is that this algorithm can work with continuous variables instead of the commonly used discrete variables.
To understand why this is a huge step forward, we need to explain the difference between continuous variables and discrete variables. The first ones have an infinite number of possible values on a closed interval while the second ones have only a finite number of values. When a quantum machine learning algorithm can work with continuous variables, it operates much, much faster than traditional algorithms.
Some of the applications for the new algorithm include many science and engineering models, because most of them involve continuous variables.
Here’s what George Siopsis, co-author of the new study had to say about the advantages of the new algorithm: “Our work demonstrates the ability to take advantage of photonics to perform machine learning tasks on a quantum computer that could far exceed the speed of any conventional computer.” Siopsis added that quantum machine learning has other advantages as well; some of them include lower energy requirements, the ability to store more information per qubit, as well as a low cost per qubit compared to other technologies.