Efficient MOE inference and training

We propose Qplacer, a frequency-aware electrostatic-based placement framework tailored for superconducting quantum computers, to alleviate crosstalk by isolating these components in spatial and frequency domains alongside compact substrate design.

We develop qGDP to legalize quantum components by adhering to quantum spatial constraints and reducing resonator crossing to alleviate various crosstalk effects.

We introduce the Flexible Hadamard Test, which, when applied to first-order gradient estimation methods, can invert the roles of ansatz generators and observables. This inversion facilitates the use of measurement optimization techniques to efficiently compute PQC gradients.

Survey on AI for QC

We explore developing a \texttt{H}eterogeneous-aware \texttt{EX}pert \texttt{A}llocation framework, \textbf{\texttt{HEXA-MoE}}, with significantly enhanced computing efficiency.