A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG

2018-08-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Quantum Computing · Depth: Expert, extended

Summary

PennyLang introduces a novel, open-source dataset of 3,347 PennyLane-specific quantum code samples, curated to enhance Large Language Model (LLM) capabilities in quantum software development. Unlike existing efforts focused on Qiskit, this dataset broadens AI-driven code assistance to the PennyLane framework, a leading platform for hybrid quantum-classical computing. The dataset was automatically created by leveraging quantum computing textbooks, official documentation, and open-source repositories, followed by a systematic methodology for data refinement, annotation, and formatting. An evaluation using a Retrieval-Augmented Generation (RAG) framework demonstrated that integrating this dataset significantly improves PennyLane code generation, with models like GPT-4o Mini, Claude 3.5 Sonnet, and Qwen 2.5 showing performance gains of 11.67%, 7.69%, and 14.38% respectively in functionality, syntax, and modularity.

Key takeaway

For Machine Learning Engineers developing quantum code assistants, integrating the PennyLang dataset and a RAG framework can significantly improve the accuracy, syntax, and modularity of generated PennyLane code. You should consider adopting this methodology to reduce development friction and enhance the quality of AI-assisted quantum programming, especially for hybrid quantum-classical systems.

Key insights

A new PennyLane-specific dataset significantly improves LLM-based quantum code generation via Retrieval-Augmented Generation.

Principles

• High-quality, domain-specific datasets are crucial for LLM performance.
• RAG enhances LLM accuracy and adherence to best practices.
• Systematic data curation improves LLM training efficiency.

Method

Data collection from GitHub, books, and documentation, followed by refinement, annotation (using GPT-4o API for instruction-query format), tokenization, and RAG-based evaluation using LangChain and Chroma DB.

In practice

• Use RAG to ground LLM responses in domain-specific datasets.
• Annotate code samples with contextual descriptions for better LLM training.
• Employ left padding for autoregressive models to maintain causal attention.

Topics

• Quantum Computing
• Large Language Models
• PennyLane Framework
• Retrieval-Augmented Generation
• Quantum Code Generation

Code references

• Qiskit/textbook
• qiskit-community/ibm-quantum-challenge-2024

Best for: AI Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.