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QI: Fachverband Quanteninformation

QI 12: Quantum Computing Theory II

QI 12.2: Vortrag

Dienstag, 11. März 2025, 11:30–11:45, HS IV

Quantum Optimization using LR-QAOA — •Karthik Jayadevan, Vanessa Dehn, and Thomas Wellens — Fraunhofer Institut für Angewandte Festkörperphysik (IAF)

The Quantum Approximate Optimization Algorithm (QAOA) is a promising approach for solving Combinatorial Optimization Problems (COPs) potentially more efficiently than classical algorithms. However, standard QAOA faces challenges owing to the complexity of optimizing the variational parameters, which itself is an NP-hard optimization problem [1], thus limiting its expected advantage. Recent work has suggested that fixed linear ramp schedules could serve as a universal set of QAOA parameters, potentially offering scaling advantages [2]. In this study, we investigate the application of a modified QAOA variant utilizing Linear Ramp QAOA (LR-QAOA) to certain COPs. Since LR-QAOA significantly reduces the parameter optimization complexity, it enables the determination of good candidates for circuit parameters through extrapolation from smaller to larger problem sizes. Further, we examine the runtime scaling of LR-QAOA for these use cases and compare it with the best-known classical methods.

[1] L. Bittel and M. Kliesch, Physical review letters 127, 120502 (2021).

[2] J. A. Montanez-Barrera and K. Michielsen, Towards a universal QAOA protocol: Evidence of a scaling advantage in solving some combinatorial optimization problems, 2024.

Keywords: quantum optimization; QAOA; QUBO