Classification problems as well as image recognition by means of machine vision are of great relevance in industry. We can think, for example, of the classification of materials suitable/unsuitable for the manufacture of a certain product or the detection of manufacturing faults from an image of the final product. Artificial intelligence already solves such problems very well. However, artificial intelligence in combination with quantum computing promises to improve accuracy and time over traditional artificial intelligence, especially in complex classification problems. In this case, two quantum computing technologies are involved: on the one hand, quantum annealing for the optimisation of traditional classifiers; on the other hand, gate-based quantum computing for the development of quantum or hybrid classifiers. If you want to know more about these technologies, you can read our white paper.
Quantum machine learning faces challenges with current quantum hardware, especially in generating high-dimensional data such as images. The Quantum Patch GAN offers a solution by generating images in smaller segments or "patches".
A random walk is a type of algorithmic process that involves taking random steps towards new points, with the probability of reaching a given point being dependent only on the previous point.
The generation of random numbers has been a subject of interest to human beings since ancient times. Proof of this is the existence of objects such as the dice, whose origin is so distant that it is not exactly known to us.