Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code
Nicolas Dupuis, Luca Buratti, Sanjay Vishwakarma, Aitana Viudes, Forrat, David Kremer, Ismael Faro, Ruchir Puri, and Juan Cruz-Benito

TL;DR
This paper presents training a specialized large language model for quantum computing code generation using Qiskit, addressing unique challenges and evaluating its performance with a custom quantum programming benchmark.
Contribution
The paper introduces a quantum computing-specific Code LLM trained on Qiskit, along with a new benchmark for evaluating quantum code generation performance.
Findings
Our model outperforms existing models on quantum tasks.
The custom benchmark effectively measures quantum code generation quality.
Examples demonstrate improved code suggestions for quantum programming.
Abstract
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…
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Taxonomy
TopicsComputational Physics and Python Applications · Scientific Computing and Data Management
MethodsSparse Evolutionary Training
