Dresden 2026 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
QI: Fachverband Quanteninformation
QI 1: Quantum Computing and Algorithms I
QI 1.7: Vortrag
Montag, 9. März 2026, 11:30–11:45, BEY/0137
Measurement-driven Quantum Approximate Optimization — •Tobias Stollenwerk1 and Stuart Hadfield2,3 — 1Institute for Quantum Computing Analytics (PGI-12), Jülich Research Centre, Wilhelm-Johnen-Straße, 52428 Jülich, Germany — 2Quantum Artificial Intelligence Lab (QuAIL), NASA Ames Research Center, Moffett Field, CA 94035, USA — 3USRA Research Institute for Advanced Computer Science (RIACS), Mountain View, CA 94043, USA
Algorithms based on non-unitary evolution have attracted much interest for ground state preparation on quantum computers. In this work we specialize and extend one recently proposed approach that employs mid-circuit measurements and control to the setting of constrained and unconstrained combinatorial optimization. For this we compare and contrast both penalty-based and feasibility-preserving approaches, elucidating the significant advantages of the latter approach. We show how to select parameters such that the success probability of each measurement step is bounded away from 1/2. Our approach is general and may be applied to easy-to-prepare initial states as a standalone algorithm, or deployed as a quantum postprocessing stage. We then propose a more sophisticated variant of our algorithm that adaptively applies a mixing operator or not, based on the measurement outcomes seen so far, as to speeds up the algorithm and helps the system evolution avoid slowing down or getting stuck suboptimally. In particular, we show that mixing operators from QAOA can be imported directly, both for the necessary eigenstate scrambling operator and for initial state preparation, and discuss quantum resource tradeoffs.
Keywords: Quantum Optimization; Measurement-based Quantum Computation; NISQ