Ethical Considerations of LLM-Driven Quantum Code Generation for Optimization Tasks
Independent Researcher , USA, Jyoti Kunal Shah
Abstract
The convergence of large language models (LLMs) and quantum computing has the potential to revolutionize software development for quantum optimization tasks. AI-assisted code generation, powered by models like OpenAI Codex, can accelerate the design of quantum algorithms by automating routine coding tasks and democratizing access to quantum programming. However, this innovation introduces a web of ethical, legal, and technical challenges. This paper investigates the implications of using LLMs to generate quantum code, focusing on intellectual property (IP) concerns, the risk of unintended outcomes, legal ambiguity, and dual-use scenarios. We propose an ethical architecture for responsible AI-assisted development, incorporating human-in-the-loop systems, license-compliance mechanisms, and auditing tools. Case studies illustrate potential failures in code correctness, security, and attribution. We conclude with recommendations for explainable AI systems, curated datasets, and governance models that ensure innovation without sacrificing safety or compliance. By addressing these concerns proactively, the community can guide LLM-powered quantum development toward a responsible future.