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Which shortcuts actually work for tuning quantum optimization circuits?
Maosheng Guo, Joel Jurado Diaz, Anurag Ramesh, Conrad J. Haupt, Alberto Baiardi, Dimitrios Athanasakos, M. Emre Sahin, Oscar Wallis, George Pennington, Christian Arenz, Sebastian Brandhofer, Georgios Korpas, Ieva Čepaitė, J. A. Montañez-Barrera, Jakub Marecek, Davide Venturelli, Stephan Eidenbenz, David E. Bernal Neira, Daniel J. Egger
June 3, 2026
Quantum approximate optimization needs circuit parameters tuned just right, but no one knew which tuning method wins at real scale (100+ qubits). Testing approximation techniques like matrix product states and "train small, transfer big" strategies, then validating on actual quantum hardware, the team found clear operational winners that depend on available compute budget. The result is a practical decision guide for running these algorithms end-to-end on today's machines.
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