  Large Language Models (LLMs) contribute significantly to the development of
conversational AI and has great potentials to assist the scientific research in
various areas. This paper attempts to address the following questions: What
opportunities do the current generation of generative pre-trained transformers
(GPTs) offer for the developments of noisy intermediate-scale quantum (NISQ)
technologies? Additionally, what potentials does the forthcoming generation of
GPTs possess to push the frontier of research in fault-tolerant quantum
computing (FTQC)? In this paper, we implement a QGAS model, which can rapidly
propose promising ansatz architectures and evaluate them with application
benchmarks including quantum chemistry and quantum finance tasks. Our results
demonstrate that after a limited number of prompt guidelines and iterations, we
can obtain a high-performance ansatz which is able to produce comparable
results that are achieved by state-of-the-art quantum architecture search
methods. This study provides a simple overview of GPT's capabilities in
supporting quantum computing research while highlighting the limitations of the
current GPT at the same time. Additionally, we discuss futuristic applications
for LLM in quantum research.
