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

We design a set of hardware-efficient variational ansatz for quantum convolutional circuits.

We propose a method for more reliable VQE training.

We present a hybrid gate-pulse model that reduces circuit execution time for VQAs.

We develop Transformer-based ML method for blood pressure prediction from ultrasound signals.

We design quantum antivirus to detect and explore different types of quantum computer viruses and defense methods.