Redefining Quantum Machine Learning

New study from team of quantum physicists at Freie Universität Berlin challenges traditional understanding of quantum machine learning. Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to allow computers to learn without explicitly being programmed. While discussions surrounding AI algorithms, such as ChatGPT and other generative models, are taking place at all levels of society, the machine learning capabilities of quantum computers are still somewhat unexplored. Researchers around the world are currently working hard to answer the question of whether quantum computers will be able to better solve some of the problems presented by conventional machine learning. A study carried out by team of researchers from Freie Universität Berlin has now revealed remarkable insights that challenge previous assumptions about quantum machine learning. The team has discovered that neuronal quantum networks can not only learn but also memorize seemingly random data. The study, titled "Understanding Quantum Machine Learning Also Requires Rethinking Generalization" was published in the scientific journal Nature Communications and is available online at: '024 -45882-z.