Combinatorial optimization plays a crucial role in many industrial applications. While classical computing often struggles with complex instances, quantum optimization emerges as a promising alternative. Here, we present an enhanced bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm to address higher-order unconstrained binary optimization (HUBO). We apply BF-DCQO to a HUBO problem featuring three-local terms in the Ising spin-glass model, validated experimentally using 156 qubits on an IBM quantum processor. In the studied instances, our results outperform standard methods such as the quantum approximate optimization algorithm, quantum annealing, simulated annealing, and Tabu search. Furthermore, we provide numerical evidence of the feasibility of a similar HUBO problem on a 433-qubit Osprey-like quantum processor. Finally, we solve denser instances of the MAX 3-SAT problem in an IonQ emulator. Our results show that BF-DCQO offers an effective path for solving large-scale HUBO problems on current and near-term quantum processors.
@article{arxiv.2409.04477,
title = {Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization},
author = {Sebastián V. Romero and Anne-Maria Visuri and Alejandro Gomez Cadavid and Anton Simen and Enrique Solano and Narendra N. Hegade},
journal= {arXiv preprint arXiv:2409.04477},
year = {2025}
}