Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces significant redundancy when quantum gates are eventually transformed into control signals and applied on quantum devices. For superconducting quantum computers, the control signals are microwave pulses. Therefore, pulse-level optimization has gained more attention from researchers due to their advantages in terms of circuit duration. Recent works, however, are limited by their poor scalability brought by the large parameter space of control signals. In addition, the lack of gate-level "knowledge" also affects the performance of pure pulse-level frameworks. We present a hybrid gate-pulse model that can mitigate these problems. We propose to use gate-level compilation and optimization for "fixed" part of the quantum circuits and to use pulse-level methods for problem-agnostic parts. Experimental results demonstrate the efficiency of the proposed framework in discrete optimization tasks. We achieve a performance boost at most 8% with 60% shorter pulse duration in the problem-agnostic layer.
@inproceedings{liang2023hybrid,
title={Hybrid gate-pulse model for variational quantum algorithms},
author={Liang, Zhiding and Song, Zhixin and Cheng, Jinglei and He, Zichang and Liu, Ji and Wang, Hanrui and Qin, Ruiyang and Wang, Yiru and Han, Song and Qian, Xuehai and others},
booktitle={2023 60th ACM/IEEE Design Automation Conference (DAC)},
pages={1--6},
year={2023},
organization={IEEE}
}
The authors would like to thank Naoki Kanazawa for insightful discussions on pulse-level algorithm implementation and design through Qiskit-OpenPulse, Jiaqi Leng and Yuxiang Peng for discussion on pulse-level gradient descent methods. We acknowledge the use of IBM Quantum services for this work.
