QCircuitNet: A Large-Scale Hierarchical Dataset for Quantum Algorithm Design

Rui Yang, Yuntian Gu, Ziruo Wang, Yitao Liang, Tongyang Li

Keywords: AI for science, quantum computing, quantum oracle construction, quantum algorithm design

Abstract:

Quantum computing is an emerging field recognized for the significant speedup it offers over classical computing through quantum algorithms. However, designing and implementing quantum algorithms pose challenges due to the complex nature of quantum mechanics and the necessity for precise control over quantum states. To address these challenges, we leverage AI to simplify and enhance the process. Despite the significant advancements in AI, there has been a lack of datasets specifically tailored for this purpose.

In this work, we introduce QCircuitNet, a benchmark and test dataset designed to evaluate AI’s capability in designing and implementing quantum algorithms in the form of quantum circuit codes. Unlike traditional AI code writing, this task is fundamentally different and significantly more complicated due to the highly flexible design space and the extreme demands for intricate manipulation of qubits.

Our key contributions include:
1. The first comprehensive, structured universal quantum algorithm dataset.
2. A framework which formulates the task of quantum algorithm design for Large Language Models (LLMs), providing guidelines for expansion and potential evolution into a training dataset.
3. Automatic validation and verification functions, allowing for scalable and efficient evaluation methodologies.
4. A fair and stable benchmark that avoids data contamination, a particularly critical issue in quantum computing datasets.

Our work aims to bridge the gap in available resources for AI-driven quantum algorithm design, offering a robust and scalable method for evaluating and improving AI models in this field. As we expand the dataset to include more algorithms and explore novel fine-tuning methods, we hope it will significantly contribute to both quantum algorithm design and implementation.

Submission Number: 2285