Presentation
Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction
DescriptionMulti-agent frameworks with Large Language Models (LLMs) have become promising tools for generating general-purpose programming languages using test-driven development, allowing developers to create more accurate and robust code.
However, their potential has not been fully unleashed for domain-specific programming languages, where specific domain exhibits unique optimization opportunities for customized improvement.
In this paper,
we take the first step in exploring multi-agent code generation for quantum programs.
We demonstrate examples of AI-assisted quantum error prediction and correction, demonstrating the effectiveness of our multi-agent framework in reducing the quantum errors of generated quantum programs.
However, their potential has not been fully unleashed for domain-specific programming languages, where specific domain exhibits unique optimization opportunities for customized improvement.
In this paper,
we take the first step in exploring multi-agent code generation for quantum programs.
We demonstrate examples of AI-assisted quantum error prediction and correction, demonstrating the effectiveness of our multi-agent framework in reducing the quantum errors of generated quantum programs.
Event Type
Research Manuscript
TimeTuesday, June 242:15pm - 2:30pm PDT
Location3001, Level 3
AI
AI2: AI/ML Application and Infrastructure