Generative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy

TL;DR

This paper reviews and categorizes thirteen generative AI systems for quantum circuit and code creation, highlighting the current state, evaluation methods, and the gap in hardware-level validation.

Contribution

It provides a comprehensive taxonomy and evaluation framework for quantum generative AI, identifying key gaps in hardware deployment testing.

Findings

All systems address syntax correctness

Most systems partially address semantic correctness

None evaluate end-to-end on quantum hardware

Abstract

We review thirteen generative systems and five supporting datasets for quantum circuit and quantum code generation, identified through a structured scoping review of Hugging Face, arXiv, and provenance tracing (January-February 2026). We organize the field along two axes: artifact type (Qiskit code, OpenQASM programs, circuit graphs); crossed with training regime (supervised fine-tuning, verifier-in-the-loop RL, diffusion/graph generation, agentic optimization); and systematically apply a three-layer evaluation framework covering syntactic validity, semantic correctness, and hardware executability. The central finding is that while all reviewed systems address syntax and most address semantics to some degree, none reports end-to-end evaluation on quantum hardware (Layer 3b), leaving a significant gap between generated circuits and practical deployment. Scope note: quantum code refers throughout to quantum program artifacts (QASM, Qiskit); we do not cover generation of quantum error-correcting codes (QEC).