Overview
Quantum Machine Learning represents the intersection of quantum information science, machine learning, and scientific computing. The group's activities range from the development of quantum algorithms for optimization and inference to hybrid quantum-classical workflows, quantum-enhanced scientific observables, and workforce development in emerging quantum technologies. A distinguishing feature of this pillar is its strong connection to the other research areas. Quantum algorithms support optimization problems arising in detector design and operations, while quantum-information concepts provide entirely new observables for experimental particle physics.
Magic
The Jung group is investigating the correlation between quantum magic (AKA non-stabilizerness) and trainability of Parameterized Quantum Circuits (PQCs). Across many circuit families, we have developed a protocol which stratifies the parameter space based on magic and subsequently checks gradient behavior. We aim to understand in what cases magic helps, hurts, or has no effect on trainability of PQCs.
