Improving the Performance of Digitized Counterdiabatic Quantum Optimization via Algorithm-Oriented Qubit Mapping

Abstract

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.

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• Exploring Ground States of Fermi-Hubbard Model on Honeycomb Lattices with Counterdiabaticity
• Exploring the Fidelity of Flux Qubit Measurement in Different Bases via the Quantum Flux Parametron
• Data-Efficient Quantum Noise Modeling via Machine Learning