QiboAgent: a practitioner's guideline to open source assistants for Quantum Computing code development

• URL: http://arxiv.org/abs/2603.15538v1
• Date: Mon, 16 Mar 2026 17:05:06 GMT
• Title: QiboAgent: a practitioner's guideline to open source assistants for Quantum Computing code development
• Abstract summary: We introduce QiboAgent, a reference implementation designed to serve as a practitioner's guideline for developing coding assistants in Quantum Computing.<n>We explore how lightweight, open-source Large Language Models provided with a custom workflow architecture compare.<n>We observe that this hybrid approach significantly reduces hallucination rates in code generation compared to a proprietary baseline, achieving a peak accuracy of 90.2%.
• Score: 0.632324422931024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce QiboAgent, a reference implementation designed to serve as a practitioner's guideline for developing specialized coding assistants in Quantum Computing middleware. Addressing the limitations in scientific software development of general-purpose proprietary models, we explore how lightweight, open-source Large Language Models (LLMs) provided with a custom workflow architecture compare. In detail, we experiment with two complementary paradigms: a Retrieval-Augmented Generation pipeline for high-precision information retrieval, and an autonomous agentic workflow for complex software engineering tasks. We observe that this hybrid approach significantly reduces hallucination rates in code generation compared to a proprietary baseline, achieving a peak accuracy of 90.2% with relatively small open-source models of size up to 30B parameters. Furthermore, the agentic framework exhibits advanced coding capabilities, automating the resolution of maintenance issues and new features requests, or by prototyping larger-scale refactors of the codebase, such as producing a compiled Rust module with bindings of an original pure python package, Qibo in our case. The LLM workflows used for our analysis are integrated into a user interface and a Model Context Protocol server, providing an accessible tool for Qibo developers.

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