Towards autonomous quantum physics research using LLM agents with access to intelligent tools

• URL: http://arxiv.org/abs/2511.11752v1
• Date: Thu, 13 Nov 2025 18:18:58 GMT
• Title: Towards autonomous quantum physics research using LLM agents with access to intelligent tools
• Authors: Sören Arlt, Xuemei Gu, Mario Krenn,
• Abstract summary: We present AI-Mandel, an agent that can generate and implement ideas in quantum physics.<n> AI-Mandel formulates ideas from the literature and uses a domain-specific AI tool to turn them into concrete experiment designs.<n>The ideas include new variations of quantum teleportation, primitives of quantum networks in indefinite causal orders, and new concepts of geometric phases.
• Score: 0.19116784879310023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) is used in numerous fields of science, yet the initial research questions and targets are still almost always provided by human researchers. AI-generated creative ideas in science are rare and often vague, so that it remains a human task to execute them. Automating idea generation and implementation in one coherent system would significantly shift the role of humans in the scientific process. Here we present AI-Mandel, an LLM agent that can generate and implement ideas in quantum physics. AI-Mandel formulates ideas from the literature and uses a domain-specific AI tool to turn them into concrete experiment designs that can readily be implemented in laboratories. The generated ideas by AI-Mandel are often scientifically interesting - for two of them we have already written independent scientific follow-up papers. The ideas include new variations of quantum teleportation, primitives of quantum networks in indefinite causal orders, and new concepts of geometric phases based on closed loops of quantum information transfer. AI-Mandel is a prototypical demonstration of an AI physicist that can generate and implement concrete, actionable ideas. Building such a system is not only useful to accelerate science, but it also reveals concrete open challenges on the path to human-level artificial scientists.

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