Advanced For-Loop for QML algorithm search

TL;DR

This paper presents an advanced multi-agent framework utilizing large language models to automate the search and optimization of quantum machine learning algorithms, inspired by DeepMind's FunSearch, enabling systematic exploration of quantum adaptations of classical ML concepts.

Contribution

It introduces a novel agent-based system for automated QML algorithm search, extending classical ML concepts into quantum computing with a focus on iterative refinement.

Findings

Demonstrated potential of agentic frameworks in quantum algorithm development

Systematic exploration of classical ML concepts for quantum adaptation

Future work includes planning and strategy optimization in search space

Abstract

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.