Quantum computing represents one of the most technically dense frontiers in modern science, with specialized vocabulary spanning quantum gates, superposition, entanglement, variational quantum algorithms, and error-correction codes. Providing accurate, real-time research-grade assistance in this domain requires an LLM that is both highly capable and deployable on resource-constrained hardware. This paper presents the design and deployment of a Quantum Computing Research Agent Chatbot powered by PEFT fine-tuning of a 1.58-bit quantized LLM (BitNet b1.58). We extend the mathematical frameworks of Low-Rank Adaptation (LoRA), Adapter Layers, and Prefix Tuning to the quantum-domain fine-tuning context, incorporating a bespoke quantum-terminology corpus and a Retrieval-Augmented Generation (RAG) layer backed by a curated quantum literature index. We derive the rank–accuracy trade-off in the context of quantum NLP tasks, show that LoRA at rank r = 16 achieves an F1 score of 88.7 on quantum question-answering benchmarks while using only 0.13% of full model parameters, and demonstrate end-to-end inference at 4.2 tokens/second on a single NVIDIA T4 GPU. The proposed agent pipeline integrates with academic literature APIs (arXiv, Semantic Scholar) and supports multi-turn research dialogues, citation generation, and mathematical expression rendering.

Keywords— Quantum Computing Chatbot, PEFT, BitNet 1.58-bit Quantization, Low-Rank Adaptation, Research Agent, Retrieval-Augmented Generation, Ternary Weight Matrices, Quantum NLP