The innovation initiative QuantumBW launches the new "QuantumBW Colloquium" on the campus of the Fraunhofer Institute Center Stuttgart. The aim of the colloquium is to promote scientific exchange on hardware and algorithmic topics in the field of quantum computing, to present the latest developments in this research area and to promote the idea of co-development of quantum solutions.
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.
In addition to the question of how the next generation of computers will be realized, it is also exciting to see what next-generation computers can be used for. They have promising applications in cryptography, machine learning and optimization. Due to the currently still noisy, small systems of the NISQ era (Noisy Intermediate Scale), the class of variational quantum algorithms is particularly interesting. These and other topics such as quantum error correction, barren plateaus and quantum advantage will be discussed in the colloquium on the following dates: