SOURCE-LINKED INTELLIGENCE

quantum-enhanced shadows: scalable quantum-to-classical converters

CORDIS · observation · Publication date unknown

quantum hardware and utilizes genuine quantum effects that do not have a classical counterpart. Building on these ideas, we also establish rigorous synergies between quantum experiments and classical machine learning. Shadow learning protocols use shadows to succinctly represent training data obtained from actual quantum experiments. A classical training stage then enables data-driven learning of genuine quantum phenomena. Finally, we develop new tools to ensure reliable execution on current quantum hardware, thus bridging the gap between theory and experiment. My interdisciplinary skill set combines methods from modern computer science with quantum information and has already led to numerous high-impact contributions (e.g. 1 Nature Physics with more than 350 citations and 2 Science publications). These insights form the basis for this larger project, where we lay the foundation for scalable and practical quantum data processing and learning that can keep up and grow with future improvements in quantum technology. classical shadows, noisy quantum computation, quantum information, ri

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR