专为量子编程打造的可信聊天机器人，提升代码准确性与可靠性。
Architecture for a Trustworthy Quantum Chatbot
- 用专用LLM分类请求，搭配确定性逻辑引擎生成可靠答案
- 支持直接运行的Qiskit代码，解决旅行商等工程问题
- 架构分离生成与逻辑，适合量子计算开发者使用
基于大语言模型（LLM）的工具如ChatGPT在经典编程中虽有用，但在专业领域因训练数据不足而可靠性差。量子计算领域尤其如此，通用聊天机器人回答质量低。C4Q是一款专注于量子程序的可信聊天机器人，通过集成专用LLM进行请求分类，结合确定性逻辑引擎与问答模块，实现可靠的量子计算支持。本文介绍其最新版本2.0：提供可直接运行的Qiskit代码用于门定义与电路操作；扩展功能以解决旅行商问题、背包问题等软件工程任务；引入反馈机制实现迭代优化。后端测试验证系统可靠性，实证评估显示分类LLM接近完美准确率。与三个现有聊天机器人对比表明，C4Q 2.0在可维护性与正确性上表现更优，凸显软件架构设计（如分离确定性逻辑与概率文本生成）对结果质量的关键影响。
原文摘要 · Abstract (English)
Large language model (LLM)-based tools such as ChatGPT seem useful for classical programming assignments. The more specialized the field, the more likely they lack reliability because of the lack of data to train them. In the case of quantum computing, the quality of answers of generic chatbots is low. C4Q is a chatbot focused on quantum programs that addresses this challenge through a software architecture that integrates specialized LLMs to classify requests and specialized question answering modules with a deterministic logical engine to provide trustworthy quantum computing support. This article describes the latest version (2.0) of C4Q, which delivers several enhancements: ready-to-run Qiskit code for gate definitions and circuit operations, expanded features to solve software engineering tasks such as the travelling salesperson problem and the knapsack problem, and a feedback mechanism for iterative improvement. Extensive testing of the backend confirms the system's reliability, while empirical evaluations show that C4Q 2.0's classification LLM reaches near-perfect accuracy. The evaluation of the result consists in a comparative study with three existing chatbots highlighting C4Q 2.0's maintainability and correctness, reflecting on how software architecture decisions, such as separating deterministic logic from probabilistic text generation impact the quality of the results.
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