  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.
