Quantum Circuit Vision: Cost-Aware Evaluation of Visual AI Agents for Quantum Code Generation
Can AI agents visually comprehend quantum circuit diagrams and generate verified executable code--and at what cost? We present Quantum Circuit Vision, a cost-aware evaluation framework for multimodal AI agents on quantum circuit visual understanding. We construct a 132-circuit benchmark spanning 13 categories ($1$--$10$ qubits) with executable Amazon Braket code and unitary-fidelity verification. Evaluating three frontier Claude-family models at different capability-cost tiers with $n=5$ repeated trials, we find that the mid-tier model (Sonnet 4.6, $1.30\times$ credits) offers the most favorable balance on the cost-accuracy frontier: 91% pass rate on the core subset at 18% of the per-call...
| Authors | Dongping Liu, Aoyu Zhang, Luyao Zhang |
|---|---|
| Primary category | quant-ph |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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