Code Large Language Models (Code LLMs) have emerged as powerful tools,
revolutionizing the software development landscape by automating the coding
process and reducing time and effort required to build applications. This paper
focuses on training Code LLMs to specialize in the field of quantum computing.
We begin by discussing the unique needs of quantum computing programming, which
differ significantly from classical programming approaches or languages. A Code
LLM specializing in quantum computing requires a foundational understanding of
quantum computing and quantum information theory. However, the scarcity of
available quantum code examples and the rapidly evolving field, which
necessitates continuous dataset updates, present significant challenges.
Moreover, we discuss our work on training Code LLMs to produce high-quality
quantum code using the Qiskit library. This work includes an examination of the
various aspects of the LLMs used for training and the specific training
conditions, as well as the results obtained with our current models. To
evaluate our models, we have developed a custom benchmark, similar to
HumanEval, which includes a set of tests specifically designed for the field of
quantum computing programming using Qiskit. Our findings indicate that our
model outperforms existing state-of-the-art models in quantum computing tasks.
We also provide examples of code suggestions, comparing our model to other
relevant code LLMs. Finally, we introduce a discussion on the potential
benefits of Code LLMs for quantum computing computational scientists,
researchers, and practitioners. We also explore various features and future
work that could be relevant in this context.

本文主要研究了针对量子计算领域的 Code LLMs 的训练，包括讨论量子计算编程的独特需求、使用 Qiskit 库进行高质量量子代码的训练和模型性能的评估，并探讨了 Code LLMs 对量子计算科学家、研究人员和从业人员的潜在益处以及相关特性和未来工作。