Code-level quantum circuit generation based on large language models
Abstract
Large language models (LLMs), based on deep learning techniques, are trained on vast amounts of textual data and possess the ability to understand and generate natural language.They have found widespread application in tasks such as machine translation, text generation, and question answering.This work proposes a novel approach for code-level quantum circuit generation using LLM. By fine-tuning existing LLM, we enable it to generate quantum circuit code tailored to user specifications, offering an efficient and high-performance solution for quantum circuit design. Simulation results demonstrate that this method achieves high accuracy in generating quantum circuit codes with only a small set of training data. This work provides a new perspective on quantum circuit design. It highlights the potential of LLMs in quantum programming and algorithm automation, laying the foundation for future research and practical applications in quantum artificial intelligence.
