Discovering state-of-the-art quantum circuits with large language models
Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated with a small set of computational primitives, optimizing them in the quantum setting presents unique challenges, including a reversibility constraint absent from ordinary classical computation. Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them. We apply this framework to tasks spanning...
| Authors | Andi Gu, Alek H. Kemeny, Susanne F. Yelin et al. |
|---|---|
| Primary category | quant-ph |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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