A breakthrough in quantum system simulations has emerged, with researchers from Beijing Normal University and The Hong Kong University of Science and Technology demonstrating that large language models (LLMs) can autonomously perform tensor network simulations with about 90% accuracy. This innovative approach eliminates the requirement for extensive expertise, allowing rapid deployment of AI in specialized computational domains and accelerating advancements in quantum physics and chemistry. Utilizing a multi-agent architecture and in-context learning, the AI agents efficiently tackle complex tasks, overcoming traditional barriers linked to expertise in tensor networks. Benchmarking various LLM configurations revealed significant improvements in accuracy, efficiency, and reduced errors through collaboration among agents. This methodology not only streamlines workflows but also enhances accessibility to powerful computational tools, paving the way for automated scientific discovery. Ultimately, this research signifies a pivotal shift towards integrating AI into scientific processes, particularly in modeling complex phenomena like quantum phase transitions and photochemical reactions.
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