  Quantum programs are typically developed using quantum Software Development
Kits (SDKs). The rapid advancement of quantum computing necessitates new tools
to streamline this development process, and one such tool could be Generative
Artificial intelligence (GenAI). In this study, we introduce and use the Qiskit
HumanEval dataset, a hand-curated collection of tasks designed to benchmark the
ability of Large Language Models (LLMs) to produce quantum code using Qiskit -
a quantum SDK. This dataset consists of more than 100 quantum computing tasks,
each accompanied by a prompt, a canonical solution, a comprehensive test case,
and a difficulty scale to evaluate the correctness of the generated solutions.
We systematically assess the performance of a set of LLMs against the Qiskit
HumanEval dataset's tasks and focus on the models ability in producing
executable quantum code. Our findings not only demonstrate the feasibility of
using LLMs for generating quantum code but also establish a new benchmark for
ongoing advancements in the field and encourage further exploration and
development of GenAI-driven tools for quantum code generation.
