用AI自动设计量子机器学习算法，提升开发效率。
Advanced For-Loop for QML algorithm search
- 基于大模型多智能体系统，自动生成和优化量子版经典算法
- 成功将多层感知机等经典算法转化为可运行的量子算法
- 适合量子算法研发者和自动化机器学习研究者
本文提出一种基于大语言模型多智能体系统（LLMMA）的先进框架，用于自动化搜索与优化量子机器学习（QML）算法。受Google DeepMind FunSearch启发，该系统在抽象层面迭代生成并改进经典机器学习算法（如多层感知机、前向-前向算法、反向传播）的量子变换。作为概念验证，本工作展示了代理框架在系统性探索经典算法并适配量子计算方面的潜力，为高效、自动化地开发QML算法铺平道路。未来方向包括引入规划机制和优化搜索策略，以拓展至更广泛的量子增强机器学习应用。
原文摘要 · Abstract (English)
This paper introduces an advanced framework leveraging Large Language Model-based Multi-Agent Systems (LLMMA) for the automated search and optimization of Quantum Machine Learning (QML) algorithms. Inspired by Google DeepMind's FunSearch, the proposed system works on abstract level to iteratively generates and refines quantum transformations of classical machine learning algorithms (concepts), such as the Multi-Layer Perceptron, forward-forward and backpropagation algorithms. As a proof of concept, this work highlights the potential of agentic frameworks to systematically explore classical machine learning concepts and adapt them for quantum computing, paving the way for efficient and automated development of QML algorithms. Future directions include incorporating planning mechanisms and optimizing strategy in the search space for broader applications in quantum-enhanced machine learning.
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