Most artificial intelligence and machine learning algorithms rely on the principles of “linear algebra,” which is what quantum computing mastered. Therefore, in most of the algorithms, if used with quantum computers, the time and computing power that they need will often diminish in a logarithmic fashion (for example: 1,000 steps will only need 3 steps) which leads to the ability to process even larger amounts of data and use applications that were not possible before.

The matter is not only related to the speed of implementation of the algorithm, but also the modus operandi of quantum computing, which opens the door to developing algorithms of a different nature, as indicated by Dr. Maria Schuld. In one of her lectures, she said, “The hope is that quantum machine learning will achieve real artificial intelligence.” Therefore, investing in quantum algorithms may bear unexpected results that help in the development of artificial intelligence itself and perhaps the achievement of general artificial intelligence that we mentioned at the beginning of the book.

As an example, in image processing applications, the “traditional” algorithms process image in a sequential manner; that is, it recognizes the image part by part, but by using quantum algorithms, it may be possible to identify objects and people in the image simultaneously, which may avoid many of the errors that occur due to the sequential method.