QCircuitBench_Sample

QCircuitBench is the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms in the form of quantum circuit codes. Key contributions of QCircuitBench include: 1. A general framework which formulates the key features of quantum algorithm design task for Large Language Models. 2. Implementation for quantum algorithms from basic primitives to advanced applications, spanning 3 task suites, 23 algorithms, and 128,573 data points. 3. Automatic validation and verification functions, allowing for iterative and interactive evaluation without human inspection. This is a sampled version of the full QCircuitBench for easier access.