Solving tricky quantum optics problems with assistance from large language models

The capabilities of modern artificial intelligence (AI) as a “scientific collaborator” are explored by engaging it with three nuanced problems in quantum optics: state populations in optical pumping, resonant transitions between decaying states (the Burshtein effect), and degenerate mirrorless lasing. Through iterative dialogue, the authors observe that AI models–when prompted and corrected–can reason through complex scenarios, refine their answers, and provide expert-level guidance, closely resembling the interaction with an adept colleague. The findings highlight that AI can democratize access to sophisticated modeling and analysis, shifting the focus in scientific practice from technical mastery to the generation and testing of ideas, and reducing the time for completing research tasks from days to minutes. Large language models (LLMs) are a new and powerful tool for solving specific physics problems. Here, the authors explore their capabilities in solving three problems of increasing complexity in quantum optics, finding that with appropriate prompts and careful feedback, a well-trained LLM can be used as an effective scientific collaborator.

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Publication Details

Journal
Communications Physics
Published
2025-11-07
DOI
https://doi.org/10.1038/s42005-025-02398-2
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00

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article

Solving tricky quantum optics problems with assistance from large language models

Dmitry Budker, Bharath Hebbe Madhusudhana, Saikat Ghosh, Manas Pandey
Communications Physics
Quantum Computing Algorithms and Architecture
article

Solving tricky quantum optics problems with assistance from large language models

Dmitry Budker, Bharath Hebbe Madhusudhana, Saikat Ghosh, Manas Pandey
article en

Abstract

The capabilities of modern artificial intelligence (AI) as a “scientific collaborator” are explored by engaging it with three nuanced problems in quantum optics: state populations in optical pumping, resonant transitions between decaying states (the Burshtein effect), and degenerate mirrorless lasing. Through iterative dialogue, the authors observe that AI models–when prompted and corrected–can reason through complex scenarios, refine their answers, and provide expert-level guidance, closely resembling the interaction with an adept colleague. The findings highlight that AI can democratize access to sophisticated modeling and analysis, shifting the focus in scientific practice from technical mastery to the generation and testing of ideas, and reducing the time for completing research tasks from days to minutes. Large language models (LLMs) are a new and powerful tool for solving specific physics problems. Here, the authors explore their capabilities in solving three problems of increasing complexity in quantum optics, finding that with appropriate prompts and careful feedback, a well-trained LLM can be used as an effective scientific collaborator.

Communications PhysicsVol. 8(1)
Los Alamos National Laboratory (US), GSI Helmholtz Centre for Heavy Ion Research (DE), Johannes Gutenberg University Mainz (DE), Helmholtz Institute Mainz (DE), Indian Institute of Technology Kanpur (IN), University of California, Berkeley (US)
Department of Science and Technology, Ministry of Science and Technology, India, Los Alamos National Laboratory
Openalex Percentile: Top 83%
Quantum Computing Algorithms and Architecture
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Solving tricky quantum optics problems with assistance from large language models — Dmitry Budker, Bharath Hebbe Madhusudhana, et al. · Communications Physics (2025) | TGRS Research Map | TGRS