"May Quantum help improve quantum?"
Virginia Carnevali
École Polytechnique Fédérale de Lausanne

Looking for hidden threads, patterns, or unknown laws behind data has teased the curiosity of scientists across the most disparate fields of science. Most of the difficult problems that torment our minds have the concept of probability at stake. Quantum mechanics is based on probability. Having a computer itself built of quantum mechanical elements that obey the laws of quantum mechanics can help us bridge the gap between classical deterministic computers and the probabilistic nature of the problems we would like to simulate. Here we want to exploit the ability of quantum annealing to explore the energy landscape of a given target Hamiltonian. We present the application of such techniques to solve complex physical and chemical problems, such as solid-amorphous transitions, and to improve the performance of machine learning models in presence of small datasets, as is often the case for condensed matter and physics in general.