SAMOP 2023 – scientific programme

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QI: Fachverband Quanteninformation

QI 6: Poster I (joint session QI/Q)

QI 6.45: Poster

Monday, March 6, 2023, 16:30–19:00, Empore Lichthof

Portfolio Optimization using a Quantum Computer — •Matthias Hüls and Daniel Braun — Institut für Theoretische Physik, Eberhard Karls Universität Tübingen, Deutschland

Entering the era of Noisy Intermediate-Scale Quantum (NISQ) devices, hopes are raising to already make practical use of the existing quantum processors. While deep algorithms still fail on the error prone hardware, variational algorithms show error resilience to some extend. This makes them well suited for the NISQ technology. Therefore, popular candidates like the Quantum Approximate Optimization Algorithm (QAOA), designed to solve combinatorial optimization problems, attracted much attention in recent years. In a case study, we benchmark the performance of the QAOA for the portfolio optmimization problem. We focus on how the characteristics of a given problem instance influence the algorithms performance and deduce a criterion for distinguishing between 'easy' and 'hard' instances.