Die Innovationsinitiative QuantumBW startet das neue landesweite »QuantumBW Colloquium« auf dem Campus des Fraunhofer-Institutszentrums Stuttgart. Ziel des Colloquiums ist es, den wissenschaftlichen Austausch über Hardware- und Algorithmik-Themen im Bereich Quantencomputing zu fördern, die neuesten Entwicklungen auf diesem Gebiet vorzustellen und den Gedanken des »Co-Developments« von Quantenlösungen voranzutreiben.
With the advancement of quantum technology, researchers aim to understand if and how quantum algorithms could have advantages compared to their classical counterparts, e.g., in the context of machine learning. The investigation of possible benefits of quantum compared to classical machine learning models requires thorough theoretical as well as empirical studies. In this talk, we will firstly present an overview over potential advantages of quantum machine learning (QML) over its classical counterpart. Then, we will deep-dive into near-term quantum machine learning algorithms that are based on short-depth, parameterized quantum circuits, which are well suited for execution on near-term quantum hardware. These models are promising candidates for a set of near-term empirical studies targeted to understand the applicability of quantum machine learning. However, as shown by a variety of research training these models can become challenging, especially at increasing scale.