Frank Wilhelm-Mauch

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In the NISQ era, we are looking to approach quantum advantage with shallow algorithms, which are often variational in nature. In those cases, proofs of speedup are hard to come by and a lot of research is empirical. A particularily relevant case here is the Quantum Approximate Optimization Algorithm (QAOA). I will present its semiclassical limit, mean-field QAOA, which can be classically simulated and present a criterion for its applicability, hence allowing to identify cases when full QAOA is not promising. Another relevant case is the study of quantum manybody systems with digital algorithms. Here, I will show an improved, physics-inspired algorithm to sample the key observable for studying theses systems, the single-particle Green’s function. Work with Dmitry Bagrets, Aditi Misra-Spieldenner, Tobias Stollenwerk, Tim Bode, Peter Schuhmacher, David Headley, and Gino Bishop

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FZ Julich
Seminar Room 0.03, ETP
Contact: Simon Trebst