Richard Kueng
scalable interfaces for quantum-classical computing and learning
classical shadows of quantum states
What I do
Large-scale quantum experiments do not work in isolation. Substantial classical computing power is required to control the experiment and process the results. This necessarily creates information-transmission bottlenecks at the interface between quantum and classical realms. These bottlenecks create scalability issues that prevent us from using existing architectures to the best of their capabilities and may even impair our ability to further scale up system sizes. We adopt a unifying framework that takes into account all computing resources (quantum and classical) and develop quantum-to-classical interfaces to overcome information-transmission bottlenecks. Known as classical shadows, these have made a lasting impact on modern quantum information processing. Building on these ideas, we also establish rigorous synergies between quantum experiments and classical machine learning and strive to implement them on existing and near-term quantum processing units.
Why it matters
The scope of quantum computing is ambitious: we attempt to re-invent the wheel of how information is processed at the smallest scales possible according to the laws of nature. This change of scale comes with new opportunities, but also new challenges. It is really hard to balance advantages versus disadvantages and still get a plausible net gain over existing computer-based solution strategies. This is why we have fewer powerful quantum algorithms and quantum learning strategies than one might think. But this is also why I find research on this frontier so engaging, challenging and rewarding.