# Towards autonomous quantum physics research using LLM agents with access to intelligent tools arXiv: https://arxiv.org/abs/2511.11752 ยท HTML: https://mlsanity.com/paper/2511.11752/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. - Builds on 36 of its 93 references that are in the map. - First posted on arXiv 13 Nov 2025. ## Placed in - LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/) ## Builds on (most cited first, 30 of 36) - [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024) - [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025) - [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023) - [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019) - [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025) - [Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers](https://mlsanity.com/paper/2409.04109/) (2024) - [ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models](https://mlsanity.com/paper/2404.07738/) (2024) - [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023) - [The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning](https://mlsanity.com/paper/2412.00568/) (2024) - [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024) - [Kosmos: An AI Scientist for Autonomous Discovery](https://mlsanity.com/paper/2511.02824/) (2025) - [On scientific understanding with artificial intelligence](https://mlsanity.com/paper/2204.01467/) (2022) - [Efficient Evolutionary Search Over Chemical Space with Large Language Models](https://mlsanity.com/paper/2406.16976/) (2024) - [El Agente: An Autonomous Agent for Quantum Chemistry](https://mlsanity.com/paper/2505.02484/) (2025) - [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022) - [Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks](https://mlsanity.com/paper/2003.04299/) (2020) - [Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics](https://mlsanity.com/paper/1906.06843/) (2019) - [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://mlsanity.com/paper/2402.08640/) (2024) - [Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders](https://mlsanity.com/paper/2405.17044/) (2024) - [Real-time gravitational-wave inference for binary neutron stars using machine learning](https://mlsanity.com/paper/2407.09602/) (2024) - [The Denario project: Deep knowledge AI agents for scientific discovery](https://mlsanity.com/paper/2510.26887/) (2025) - [Agents for self-driving laboratories applied to quantum computing](https://mlsanity.com/paper/2412.07978/) (2024) - [Computer-inspired Quantum Experiments](https://mlsanity.com/paper/2002.09970/) (2020) - [Automating the Practice of Science -- Opportunities, Challenges, and Implications](https://mlsanity.com/paper/2409.05890/) (2024) - [Symmetry-Informed Governing Equation Discovery](https://mlsanity.com/paper/2405.16756/) (2024) - [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025) - [Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models](https://mlsanity.com/paper/2501.05382/) (2025) - [Meta-Designing Quantum Experiments with Language Models](https://mlsanity.com/paper/2406.02470/) (2024) - [QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL](https://mlsanity.com/paper/2510.00967/) (2025) - [Towards a Benchmark for Scientific Understanding in Humans and Machines](https://mlsanity.com/paper/2304.10327/) (2023) ## Built on by (most cited first, 7 of 7) - [Computer Architecture's AlphaZero Moment: Automated Discovery in an Encircled World](https://mlsanity.com/paper/2604.03312/) (2026) - [Toward Evaluation Frameworks for Multi-Agent Scientific AI Systems](https://mlsanity.com/paper/2603.26718/) (2026) - [Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents](https://mlsanity.com/paper/2604.00137/) (2026) - [Competing with AI Scientists: Agent-Driven Approach to Astrophysics Research](https://mlsanity.com/paper/2604.09621/) (2026) - [Lowering the implementation barrier of neutral-atom quantum computing with agentic workflows](https://mlsanity.com/paper/2607.25834/) (2026) - [Towards a new paradigm of scientific discovery with socialized artificial intelligence](https://mlsanity.com/paper/2608.02775/) (2026) - [Evaluating Verified Autonomy in Quantum Engineering](https://mlsanity.com/paper/2609.17439/) (2026) ## Abstract (arXiv) 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.