Problem
Research questions and friction points this paper is trying to address.
quantum software
quantum architecture
quantum systems
domain expertise
quantum computing
Are LLMs Good For Quantum Software, Architecture, and System Design?
🤖 AI Summary
Current quantum computing lacks mature solutions at the software, architecture, and systems levels and remains heavily reliant on expert knowledge, hindering its practical adoption. This work presents the first systematic evaluation of the reasoning capabilities of nine state-of-the-art large language models (LLMs) on quantum systems design tasks. We construct a comprehensive benchmark spanning quantum software, architecture, and systems, and compare LLM performance against graduate students from the University of Texas at Austin. Our findings reveal both the strengths and limitations of LLMs in this domain, offering empirical evidence and critical pathways for advancing AI-driven quantum systems research.
📝 Abstract
Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc. However, despite decades of research, they remain far from entering an era of utility. The lack of mature software, architecture, and systems solutions capable of translating quantum-mechanical properties of algorithms into physical state transformations on qubit devices remains a key factor underlying the slow pace of technological progress. The problem worsens due to significant reliance on domain-specific expertise, especially for software developers, computer architects, and systems engineers. To address these limitations and accelerate large-scale high-performance quantum system design, we ask:
Can large language models (LLMs) help with solving quantum software, architecture, and systems problems?
In this work, we present a case study assessing the performance of LLMs on quantum system reasoning tasks. We evaluate nine frontier LLMs and compare their performance to graduate UT Austin students on a set of quantum computing problems. Finally, we recommend several directions along which research and engineering development efforts must be pursued.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Models
Quantum Computing
System Design
AI for Science
Quantum Software
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