Automated near-term quantum algorithm discovery for molecular ground states
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesis, which utilises large language models to drive a highly distributed evolutionary process for discovering new algorithms. We focus on the ground state problem in quantum chemistry, and discover efficient quantum heuristic algorithms that solve it for molecules LiH, H2O, and F2 while exhibiting significant reductions in quantum resources relative to state-of-the-art near-term quantum algorithms. Further, we perform an interpretability study on the discovered algorithms and identify the key f...
| Authors | Fabian Finger, Frederic Rapp, Pranav Kalidindi et al. |
|---|---|
| Primary category | quant-ph |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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