  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 near-term quantum
devices validate the effectiveness of these strategies, leveraging both
algorithmic and hardware advantages. Our approach increases the approximation
ratio by an average of 4.49$\times$ without error mitigation and 84.8% with
error mitigation, while reducing CX gate count and circuit depth by 28.8% and
33.4%, respectively, compared to Qiskit and Tket. 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.

Quantum Physics