This paper introduces QuanUML, an extension of the Unified Modeling Language (UML) tailored for quantum software systems. QuanUML integrates quantum-specific constructs, such as qubits and quantum gates, into the UML framework, enabling the modeling of both quantum and hybrid quantum-classical systems. We apply QuanUML to Efficient Long-Range Entanglement using Dynamic Circuits and Shor’s Algorithm, demonstrating its utility in designing and visualizing quantum algorithms. Our approach supports model-driven development of quantum software and offers a structured framework for quantum software design. We also highlight its advantages over existing methods and discuss future improvements.

With the growing interest in quantum computing, the emergence of quantum supremacy has marked a pivotal milestone in the field. As a result, numerous quantum programming languages (QPLs) have been introduced to support the development of quantum algorithms. However, the application of Model-Driven Development (MDD) in quantum system engineering remains largely underexplored. This paper presents an MDD-based approach to support the structured design and implementation of quantum systems. Our framework enables the automatic generation of quantum code for multiple QPLs, thereby enhancing development efficiency and consistency across heterogeneous quantum platforms. The effectiveness and practicality of our approach have been demonstrated through multiple case studies.

Large language models (LLMs) have demonstrated good performance in general code generation; however, their capabilities in quantum code generation remain insufficiently studied. This paper presents QuanBench, a benchmark for evaluating LLMs on quantum code generation. QuanBench includes 44 programming tasks that cover quantum algorithms, state preparation, gate decomposition, and quantum machine learning. Each task has an executable canonical solution and is evaluated by functional correctness (Pass@K) and quantum semantic equivalence (Process Fidelity). We evaluate several recent LLMs, including general-purpose and code-specialized models. The results show that current LLMs have limited capability in generating the correct quantum code, with overall accuracy below 40% and frequent semantic errors. We also analyze common failure cases, such as outdated API usage, circuit construction errors, and incorrect algorithm logic. QuanBench provides a basis for future work on improving quantum code generation with LLMs.