Fine-tuning LLMs on PyChrono-specific data improves their success rate at generating runnable simulation code from about 40% to about 85%, compared to prompting general models.
C4Q: A Chatbot for Quantum
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abstract
Quantum computing is a growing field that promises many real-world applications such as quantum cryptography or quantum finance. The number of people able to use quantum computing is however still very small. This limitation comes from the difficulty to understand the concepts and to know how to start coding. Therefore, there is a need for tools that can assist non-expert in overcoming this complexity. One possibility would be to use existing conversational agents. Unfortunately ChatGPT and other Large-Language Models produce inaccurate results. This article presents C4Q, a chatbot that answers accurately basic questions and guides users when trying to code quantum programs. Contrary to other approaches C4Q uses a pre-trained large language model only to discover and classify user requests. It then generates an accurate answer using an own engine. Thanks to this architectural design, C4Q's answers are always correct, and thus C4Q can become a support tool that makes quantum computing more available to non-experts.
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ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono
Fine-tuning LLMs on PyChrono-specific data improves their success rate at generating runnable simulation code from about 40% to about 85%, compared to prompting general models.