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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21327 |
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| _version_ | 1866912507713552384 |
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| author | Closser, Dayton C. Kabala, Zbigniew J. |
| author_facet | Closser, Dayton C. Kabala, Zbigniew J. |
| contents | What is the fastest Artificial Intelligence Large Language Model (AI LLM) for generating quantum operations? To answer this, we present the first benchmarking study comparing popular and publicly available AI models tasked with creating quantum gate designs. The Wolfram Mathematica framework was used to interface with the 4 AI LLMs, including WolframLLM, OpenAI ChatGPT, Google Gemini, and DeepSeek. This comparison evaluates both the time taken by each AI LLM platform to generate quantum operations (including networking times), as well as the execution time of these operations in Python, within Jupyter Notebook. Our results show that overall, Gemini is the fastest AI LLM in producing quantum gate designs. At the same time, the AI LLMs tested achieved working quantum operations 80% of the time. These findings highlight a promising horizon where publicly available Large Language Models can become fast collaborators with quantum computers, enabling rapid quantum gate synthesis and paving the way for greater interoperability between two remarkable and cutting-edge technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pushing the Limits of LLMs in Quantum Operations Closser, Dayton C. Kabala, Zbigniew J. Quantum Physics What is the fastest Artificial Intelligence Large Language Model (AI LLM) for generating quantum operations? To answer this, we present the first benchmarking study comparing popular and publicly available AI models tasked with creating quantum gate designs. The Wolfram Mathematica framework was used to interface with the 4 AI LLMs, including WolframLLM, OpenAI ChatGPT, Google Gemini, and DeepSeek. This comparison evaluates both the time taken by each AI LLM platform to generate quantum operations (including networking times), as well as the execution time of these operations in Python, within Jupyter Notebook. Our results show that overall, Gemini is the fastest AI LLM in producing quantum gate designs. At the same time, the AI LLMs tested achieved working quantum operations 80% of the time. These findings highlight a promising horizon where publicly available Large Language Models can become fast collaborators with quantum computers, enabling rapid quantum gate synthesis and paving the way for greater interoperability between two remarkable and cutting-edge technologies. |
| title | Pushing the Limits of LLMs in Quantum Operations |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2507.21327 |