iCAIML PhD Student Sabrina Herbst received the Andreas Dieberger-Peter Skalicky scholarship 2025/26, which aims to promote scientific publications, to support participation in subject-specific conferences, and to cover other costs necessary for studies, in particular a research stay at home or abroad, e.g., material resources and travel expenses.

Sabrina’s research aims to develop theoretically grounded and experimentally validated learning paradigms for quantum machine learning (QML). Current QML models face fundamental trainability issues due to the absence of inductive biases and the exponential nature of the Hilbert space. By leveraging insights from efficient quantum algorithms - particularly for problems with group structure - her work seeks to incorporate meaningful inductive biases into QML architectures. Her research will focus on establishing novel, trainable QML paradigms and methods for robust performance evaluation, thereby advancing the foundational understanding of learning in quantum systems.