Improving the performance of digitized counterdiabatic quantum optimization via algorithm-oriented qubit mapping

This paper presents strategies to improve the performance of digitized counterdiabatic quantum optimization algorithms by cooptimizing gate sequences, algorithm parameters, and qubit mapping. Demonstrations on real hardware validate the effectiveness of these strategies, leveraging both algorithmic and hardware advantages. Specifically, our approach achieves an average increase in approximation ratio of $4.49\ifmmode\times\else\texttimes\fi{}$ without error mitigation and 84.8% with error mitigation compared to Qiskit and Tket on IBM quantum processors. These findings provide valuable insights into the codesign of algorithm implementation, tailored to optimize qubit mapping and algorithm parameters, with broader implications for enhancing algorithm performance on near-term quantum devices.