Trustworthy Natural Language Interfaces for Quantum Optimization: A Secure and Resilient Translation Framework

As quantum computing emerges as a powerful tool for solving combinatorial optimization problems, the absence of intuitive programming interfaces remains a major barrier to practical adoption. This work presents a secure and resilient natural language interface that translates high-level optimization prompts into QUBO (Quadratic Unconstrained Binary Optimization) formulations suitable for quantum solvers such as D-Wave. The translation pipeline leverages large language models (LLMs) and incorporates multiple verification layers, including schema validation, constraint compliance checks, and reverse translation for semantic fidelity auditing. Fidelity is quantified using cosine similarity between sentence embeddings of the original and reconstructed problem descriptions, enabling intent alignment analysis. We evaluate the system across 100 optimization instances spanning five canonical problem types: Knapsack, Traveling Salesman Problem, Graph Coloring, Seating Assignment, and Team Assignment. Results show that GPT-4 significantly outperforms GPT-3.5 in complex cases such as TSP, achieving up to 90% success rates where earlier models fail entirely. Latency measurements confirm interactive performance, with most translations completing in under 6 seconds. These results demonstrate the feasibility of trustworthy natural language interfaces for quantum optimization and provide a framework for verifiable, interpretable, and efficient translation from user intent to quantum-executable code.